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  • AI vs. User Researcher: How to Add More Value than a Robot

    The rise of artificial intelligence is shaking up every field, and user research is no exception. Large language models (LLMs) and AI-driven bots are now able to transcribe sessions, analyze feedback, simulate users, and even conduct basic interviews. It’s no wonder many UX researchers are asking, “Is AI going to take my job?” There’s certainly buzz around AI interviewers that can chat with users 24/7, and synthetic users: AI-generated personas that simulate user behavior.

    A recent survey found 77% of UX researchers are already using AI in some part of their work, signaling that AI isn’t just coming, it’s already here in the user research. But while AI is transforming how we work, the good news is that it doesn’t have to replace you as a user researcher.

    In this article, we’ll explore how user research is changing, why human researchers still have the edge, and how you can thrive (not just survive) by adding more value than a robot.

    Here’s what we will explore:

    1. User Research Will Change (But Not Disappear)
    2. Why AI Won’t Replace the Human Researcher (The Human Touch)
    3. Evolve or Fade: Adapting Your Role in the Age of AI
    4. Leverage AI as Your Superpower, Not Your Replacement
    5. Thrive with AI, Don’t Fear It

    User Research Will Change (But Not Disappear)

    AI is quickly redefining the way user research gets done. Rather than wiping out research roles, it’s automating tedious chores and unlocking new capabilities. Think about tasks that used to gobble up hours of a researcher’s time: transcribing interview recordings, sorting through survey responses, or crunching usage data. Today, AI tools can handle much of this heavy lifting in a fraction of the time:

    • Automated transcription and note-taking: Instead of frantically scribbling notes, researchers can use AI transcription services (e.g. Otter.ai or built-in tools in platforms like Dovetail) to get near-instant, accurate transcripts of user interviews. Many of these tools even generate initial summaries or highlight reels of key moments.
    • Speedy analysis of mountains of data: AI excels at sifting through large datasets. It can summarize interviews, cluster survey answers by theme, and flag patterns much faster than any person. For example, an AI might analyze thousands of open-ended responses and instantly group them into common sentiments or topics, saving you from manual sorting.
    • Content generation and research prep: Need a draft of a research plan or a list of interview questions? Generative AI can help generate first drafts of discussion guides, survey questions, or test tasks for you to refine.
    • Simulated user feedback: Emerging tools even let you conduct prototype tests with AI-simulated users. For instance, some AI systems predict where users might click or get confused in a design, acting like “virtual users” for quick feedback. This can reveal obvious usability issues early on (though it’s not a replacement for testing with real people, as we’ll discuss later).
    • AI-assisted reporting: When it’s time to share findings, AI can help draft research reports or create data visualizations. ChatGPT and similar models are “very good at writing”, so they can turn bullet-point insights into narrative paragraphs or suggest ways to visualize usage data. This can speed up the reporting process – just be sure to fact-check and ensure sensitive data isn’t inadvertently shared with public AI services.

    In short, AI is revolutionizing parts of the UX research workflow. It’s making research faster, scaling it up, and freeing us from busywork. By automating data collection and analysis, AI enhances productivity, freeing up a researcher’s time” to focus on deeper analysis and strategic work. And it’s not just hype: companies are already taking advantage.

    According to Greylock, by using an AI interviewer, a team can scale from a dozen user interviews a week to 20+ without adding staff. Larger organizations aren’t cutting their research departments either, they’re folding AI into their research stack to cover more ground. These teams still run traditional studies, but use AI to “accelerate research in new markets (e.g. foreign languages), spin up projects faster, and increase overall velocity”, all without expanding team size. In both cases, AI is not just replacing work, it’s expanding the scope and frequency of research. What used to be a quarterly study might become a continuous weekly insight stream when AI is picking up the slack.

    The bottom line: User research isn’t disappearing – it’s evolving. Every wave of new tech, from cloud collaboration to remote testing platforms, has changed how we do research, but never why we do it. AI is simply the latest step in that evolution. In the age of AI, the core mission of UX research remains at vital as ever: understanding real users to inform product design. The methods will be more efficient, and the scale might be greater, but human-centered insight is still the goal.

    Check it out: We have a full article on AI User Feedback: Improving AI Products with Human Feedback

    Why AI Won’t Replace the Human Researcher (The Human Touch)

    So if AI can do all these incredible things, transcribe, analyze, simulate, what’s left for human researchers to do? The answer: all the most important parts. The truth is that AI lacks the uniquely human qualities that make user researchers invaluable. It’s great at the “what,” but struggles with the “why.”

    Here are a few critical areas where real user researchers add value that robots can’t:

    • Empathy and Emotional Intelligence:  At its core, user research is about understanding people: their feelings, motivations, frustrations. AI can analyze sentiment or detect if a voice sounds upset, but it “can’t truly feel what users feel”. Skilled researchers excel at picking up tiny cues in body language or tone of voice. We notice when a participant’s voice hesitates or their expression changes, even if they don’t verbalize a problem.

      There’s simply no substitute for sitting with a user, hearing the emotion in their stories, and building a human connection. This empathy lets us probe deeper and adjust on the fly, something an algorithm following a script won’t do.
    • Contextual and Cultural Understanding: Users don’t operate in a vacuum; their behaviors are shaped by context: their environment, culture, and personal experiences. An AI bot might see a pattern (e.g. many people clicked the wrong button), but currently struggles to grasp the context behind it. Maybe those users were on a noisy subway using one hand, or perhaps a cultural norm made them reluctant to click a certain icon.

      Human researchers have the contextual awareness to ask the right follow-up questions and interpret why something is happening. We understand nuances like cultural communication styles (e.g. how a Japanese user may be too polite to criticize a design openly) and we can adapt our approach accordingly. AI, at least in its current form, can’t fully account for these subtleties.
    • Creativity and Critical Thinking: Research often involves open-ended problem solving, from designing clever study methodologies to synthesizing disparate findings into a new insight. AI is brilliant at pattern-matching but not at original thinking. It “struggles to think outside the box”, whereas a good researcher can connect dots in novel ways. We generate creative questions on the spot, improvise new tests when something unexpected happens, and apply judgement to identify what truly matters. The human intuition that sparks an “aha” moment or a breakthrough idea is not something you can automate.
    • Communication and Storytelling: One of the most important roles of a UX researcher is translating data into a compelling story for the team. We don’t just spit out a report; we tailor the message to the audience, provide rich examples, and persuade stakeholders to take action. Sure, an AI can produce a neatly formatted report or slide deck. But can it step into a meeting, read the room, and inspire the team to empathize with users?

      The art of evangelizing user insights throughout an organization – getting that engineer to feel the user’s pain, or that executive to rethink a strategy after hearing a user quote relies on human communication skills.
    • Ethics and Trust: User research frequently delves into personal, sensitive topics. Participants need to trust the researcher to handle their information with care and empathy. Human researchers can build rapport and know when to pause or change approach if someone becomes uncomfortable. An AI interviewer, on the other hand, has no lived experience to guide empathy: it will just keep following its protocol.

      Ethical judgement, i.e. knowing how to ask tough questions sensitively, or deciding when not to pursue a line of questioning remains a human strength. Moreover, over-relying on AI can introduce risks of bias or false confidence in findings. AI might sometimes give answers that sound authoritative but are misleading if taken out of context. It takes a human researcher to validate and ensure insights are genuinely true, not just fast.

    In summary, user research is more than data, it’s about humans. You can automate the data collection and number crunching, but you can’t automate the human understanding. AI might detect that users are frustrated at a certain step, but it won’t automatically know why, nor will it feel that frustration the way you can. And importantly, it “cannot replicate the surprises and nuances” that real users bring. Those surprises are often where the game-changing insights lie. 

    “The main reason to conduct user research is to be surprised”, veteran researcher Jakob Nielsen reminds us. If we ever tried to rely solely on simulated or average user behavior, we’d miss those curveballs that lead to real innovation. That’s why Nielsen believes replacing humans in user research is one of the few areas that’s likely to be impossible forever.

    User research needs real users. AI can be a powerful assistant, but it’s not a wholesale replacement for the human researcher or the human user.

    Evolve or Fade: Adapting Your Role in the Age of AI

    Given that AI is here to stay, the big question is how to thrive as a user researcher in this new landscape. History has shown that when new technologies emerge, those who adapt and leverage the tools tend to advance, while those who stick stubbornly to old ways risk falling behind.

    Consider the analogy of global outsourcing: years ago, companies could hire cheaper labor abroad for various tasks, sparking fears that many jobs would vanish. And indeed, some routine work did get outsourced. But many professionals kept their jobs, and even grew more valuable, by being better than the cheaper alternative. They offered local context, higher quality, and unique expertise that generic outsourced labor couldn’t match. The same can apply now with AI as the “cheaper alternative.” If parts of user research become automated or simulated, you need to make sure your contribution goes beyond what the automation can do. In other words, double down on the human advantages we outlined earlier (empathy, context, creativity, interpretation) and let the AI handle the repetitive grunt work.

    The reality is that some researchers who fail to adapt may indeed see their roles diminished. For example, if a researcher’s job was solely to conduct straightforward interviews and write basic reports, a product team might conclude that an AI interviewer and auto-generated report can cover the basics. Those tasks alone might not justify a full-time role in the future. However, other researchers will find themselves moving into even more impactful (and higher-paid) positions by leveraging AI.

    By embracing AI tools, a single researcher can now accomplish what used to take a small team: analyzing more data, running more studies, and delivering insights faster. This means researchers who are proficient with AI can drive more strategic value. They can focus on synthesizing insights, advising product decisions, and tackling complex research questions, rather than toiling over transcription or data cleanup. In essence, AI can elevate the role of the user researcher to be more about strategy and leadership of research, and less about manual execution. Those who ride this wave will be at the cutting edge of a user research renaissance, often becoming the go-to experts who guide how AI is integrated ethically and effectively into the process. And companies will pay a premium for researchers who can blend human insight with AI-powered scale.

    It’s also worth noting that AI is expanding the reach of user research, not just threatening it. When research becomes faster and cheaper, more teams start doing it who previously wouldn’t. Instead of skipping research due to cost or time, product managers and designers are now able to do quick studies with AI assistance. The result can be a greater appreciation for research overall – and when deeper issues arise, they’ll still call in the human experts. The caveat is that the nature of the work will change. You might be overseeing AI-driven studies, curating and validating AI-generated data, and then doing the high-level synthesis and storytelling. The key is to position yourself as the indispensable interpreter and strategist.

    Leverage AI as Your Superpower, Not Your Replacement

    To thrive in the age of AI, become a user research who uses AI – not one who completes with it. The best way to add more value than a robot is to partner with the robots and amplify your impact. Here are some tips for how and when to use AI in your user research practice:

    • Use AI to do more, faster – then add your expert touch. Take advantage of AI tools to handle the labor-intensive phases of research. For example, let an AI transcribe and even auto-tag your interview recordings to give you a head start on analysis. You can then review those tags and refine them using your domain knowledge.

      If you have hundreds of survey responses, use an AI to cluster themes and pull out commonly used phrases. Then dig into those clusters yourself to understand the nuances and pick illustrative quotes. The AI will surface the “what”; you bring the “why” and the judgement. This way, you’re working smarter, not harder – covering more ground without sacrificing quality.
    • Know when to trust AI and when to double-check. AI can sometimes introduce biases or errors, especially if it’s trained on non-representative data or if it “hallucinates” an insight that isn’t actually supported by the data. Treat AI outputs as first drafts or suggestions, not gospel truth. For instance, if a synthetic user study gives you a certain finding, treat it as a hypothesis to validate with real users – not a conclusion to act on blindly.

      As Nielsen Norman Group advises, “supplement, don’t substitute” AI-generated research for real research. Always apply your critical thinking to confirm that insights make sense in context. Think of AI as a junior analyst: very fast and tireless, but needing oversight from a human expert.
    • Employ AI in appropriate research phases. Generative AI “participants” can be handy for early-stage exploration – for example, to get quick feedback on a design concept or to generate personas that spark empathy in a pinch. They are useful for desk research and hypothesis generation, where “fake research” might be better than no research to get the ball rolling.

      However, don’t lean on synthetic users for final validation or high-stakes decisions. They often give “shallow or overly favorable feedback” and lack the unpredictable behaviors of real humans. Use them to catch low-hanging issues or to brainstorm questions, then bring in real users for the rigorous testing. Similarly, an AI interviewer (moderator) can conduct simple user interviews at scale: useful for collecting a large volume of feedback quickly, or reaching users across different time zones and languages. For research that requires deep probing or sensitive conversations, you’ll likely still want a human touch. Mix methods thoughtfully, using AI where it provides efficiency, and humans where nuance is critical.
    • Continue developing uniquely human skills. To add more value than a robot, double-down on the skills that make you distinctly effective. Work on your interview facilitation and observation abilities – e.g., reading body language, making participants comfortable enough to open up, and asking great follow-up questions. These are things an AI can’t easily replicate, and they lead to insights an AI can’t obtain.

      Similarly, hone your storytelling and visualization skills to communicate research findings in a persuasive way within your organization. The better you are at converting data into understanding and action, the more indispensable you become. AI can crunch numbers, but “it can’t sit across from a user and feel the ‘aha’ moment”, and it can’t rally a team around that “aha” either. Make sure you can.
    • Stay current with AI advancements (and limitations). AI technologies will continue to improve, so a thriving researcher keeps up with the trends. Experiment with new tools – whether it’s an AI that can analyze video recordings for facial expressions, or a platform that integrates chatGPT into survey analysis and see how they might fit into your toolkit. At the same time, keep an eye on where AI still falls short.

      For example, today’s language models still struggle to analyze visual behavior or complex multi-step interactions reliably. Those are opportunities for you to step in. Understanding what AI can and cannot do for research helps you strategically allocate tasks between you and the machine. Being knowledgeable about AI also positions you as a forward-thinking leader in your team, able to guide decisions about which tools to adopt and how to use them responsibly.

    By integrating AI into your workflow, you essentially become what Jakob Nielsen calls a “human-AI symbiont,”where “any decent researcher will employ a profusion of AI tools to augment skills and improve productivity.” Rather than being threatened by the “robot,” you are collaborating with the robot. This not only makes your work more efficient, but also more impactful – freeing you to engage in higher-level research activities that truly move the needle.

    Check it out: We have a full article on Recruiting Humans for AI User Feedback

    Conclusion: Thrive with AI, Don’t Fear It

    The age of AI, synthetic users, and robot interviewers is upon us, but this doesn’t spell doom for the user researcher – far from it. User research will change, but it will continue to thrive with you at the helm, so long as you adapt. Remember that “UX without real-user research isn’t UX”, and real users need human researchers to understand them. Your job is to ensure you’re bringing the human perspective that no AI can replicate, while leveraging AI for what it does do well. If you can master that balance, you’ll not only survive this AI wave, you’ll ride it to new heights in your career.

    In practical terms: embrace AI as your assistant, not your replacement. Let it turbocharge your workflow, extend your reach, and handle the drudge work, but keep yourself firmly in the driver’s seat when it comes to insight, empathy, and ethical judgment.

    The only researchers who truly lose out will be those who refuse to adapt or who try to complete with AI on tasks that AI does better. Don’t be that person. Instead, focus on adding value that a robot cannot: be the researcher who understands the why behind the data, who can connect with users on a human level, and who can turn research findings into stories and strategies that drive product success.

    Finally, take heart in knowing that the essence of our profession is safe. By reframing our unique value-add and wielding AI as a tool, user researchers can not only survive the AI revolution, but lead the way in a new era of smarter, more scalable, and still deeply human-centered research.

    In the end, AI won’t replace you – but a user researcher who knows how to harness AI just might. So make sure that researcher is you.

    Have questions? Book a call in our call calendar.

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  • Crowdsourced Testing: When and How to Leverage Global Tester Communities

    Crowdsourced Testing to the Rescue:

    Imagine preparing to launch a new app or feature and wanting absolute confidence it will delight users across various devices and countries. Crowdsourced testing can make this a reality. In simple terms, crowdtesting is a software testing approach that leverages a community of independent testers. Instead of relying solely on an in-house QA team, companies tap into an on-demand crowd of real people who use their own devices in real environments to test the product. In other words, it adds fresh eyes and a broad range of perspectives to your testing process, beyond what a traditional QA lab can offer.

    In today’s fast-paced, global market, delivering a high-quality user experience is paramount. Whether you need global app testing, in-home product testing, or user-experience feedback, crowdtesting can be the solution. By tapping into a large community of testers, organizations can get access to a broader spectrum of feedback, uncovering elusive issues and enabling more accurate real-world user testing. Issues that might slip by an internal team (due to limited devices, locations, or biases) can be caught by diverse testers who mirror your actual user base.

    In short, crowdsourced testing helps ensure your product works well for everyone, everywhere – a crucial advantage for product managers, engineers, user researchers, and entrepreneurs alike. In the sections below, we’ll explore how crowdtesting differs from traditional QA, its key benefits (from real-world feedback to cost and speed), when to leverage it, tips on choosing a platform (including why many turn to BetaTesting), how to run effective crowdtests, and the challenges to watch out for.

    Here’s what we will explore:

    1. Crowdsourced Testing vs. Traditional QA
    2. Key Benefits of Crowdsourced Testing
    3. When Should You Use Crowdsourced Testing?
    4. Choosing a Crowdsourced Testing Platform (What to Look For)
    5. Running Effective Crowdsourced Tests and Managing Results
    6. Challenges of Crowdsourced Testing and How to Address Them

    Crowdsourced Testing vs. Traditional QA

    Crowdsourced testing isn’t meant to completely replace a dedicated QA team, but it does fill important gaps that traditional testing can’t always cover. The fundamental difference lies in who is doing the testing and how they do it:

    • Global, diverse testers vs. in-house team: Traditional in-house QA involves a fixed team of testers (or an outsourced team) often working from one location. By contrast, crowdtesting gives you a global pool of testers with different backgrounds, languages, and devices. This means your product is checked under a wide range of real-world conditions. For example, a crowdtesting company can provide testers on different continents and carriers to see how your app performs on various networks and locales – something an in-house team might struggle with.
    • On-demand scalability vs. fixed capacity: In-house QA teams have a set headcount and limited hours, so scaling up testing for a tight deadline or a big release can be slow and costly (hiring and training new staff). Crowdsourced testing, on the other hand, is highly flexible and scalable – you can ramp up the number of testers in days or even hours. Need overnight testing or a hundred extra testers for a weekend? The crowd is ready, thanks to time zone coverage and sheer volume.
    • Real devices & environments vs. lab setups: Traditional QA often uses a controlled lab environment with a limited set of devices and browsers. Crowdsourced testers use their own devices, OS versions, and configurations in authentic environments (home, work, different network conditions). This helps uncover device-specific bugs or usability issues that lab testing might miss.

      As an example, testing with real users in real environments may reveal that your app crashes on a specific older Android model or that a website layout breaks on a popular browser under certain conditions – insights you might not get without that diversity.
    • Fresh eyes and user perspective vs. product familiarity: In-house testers are intimately familiar with the product and test scripts, which is useful but can also introduce blind spots. Crowdsourced testers approach the product like real users seeing it for the first time. They are less biased by knowing how things “should” work. This outsider perspective can surface UX problems or assumptions that internal teams might gloss over.

    It’s worth noting that traditional QA still has strengths – for example, in-house teams have deep product knowledge and direct communication with developers. The best strategy is often to combine in-house and crowdtesting to get the benefits of both. Crowdsourced testing excels at broad coverage, speed, and real-world realism, while your core QA team can focus on strategic testing and integrating results. Many organizations use crowdtesting to augment their QA, not necessarily replace it.

    Natural Language Processing (NLP) is one of the AI terms startups need to know. Check out the rest here in this article: Top 10 AI Terms Startups Need to Know

    Key Benefits of Crowdsourced Testing

    Now let’s dive into the core benefits of crowdtesting and why it’s gaining popularity across industries. In essence, it offers three major advantages over traditional QA models: real-world user feedback, speed, and cost-effectiveness(along with scalability as a bonus benefit). Here’s a closer look at each:

    • Authentic, Real-World Feedback: One of the biggest draws of crowdtesting is getting unbiased input from real users under real-world conditions. Because crowd testers come from outside your company and mirror your target customers, they will use your product in ways you might not anticipate. This often reveals usability issues, edge-case bugs, or cultural nuances that in-house teams can overlook.

      For instance, a crowd of testers in different countries can flag localization problems or confusing UI elements that a homogeneous internal team might miss. In short, crowdtesting helps ensure your product is truly user-friendly and robust in the wild, not just in the lab.
    • Faster Testing Cycles and Time-to-Market: Crowdsourced testing can dramatically accelerate your QA process. With a distributed crowd, you can get testing done 24/7 and in parallel. While your office QA team sleeps, someone on the other side of the world could be finding that critical bug. Many crowd platforms let you start a test and get results within days or even hours.

      For example, you might send a build to the crowd on Friday and have a full report by Monday. This round-the-clock, parallel execution leads to “faster test cycles”, enabling quicker releases. Faster feedback loops mean bugs are found and fixed sooner, preventing delays. In an era of continuous delivery and CI/CD, this speed is a game-changer for product teams racing to get updates out.
    • Cost Savings and Flexibility: Cost is a consideration for every team, and crowdtesting can offer significant savings. Instead of maintaining a large full-time QA staff (with salaries, benefits, and idle time between releases), crowdtesting lets you pay only for what you use. Need a big test cycle this month and none next month? With a crowd platform, that’s no problem – you’re not carrying unutilized resources. Additionally, you don’t have to invest in an extensive device lab; the crowd already has thousands of device/OS combinations at their disposal.

      Many platforms also offer flexible pricing models (per bug, per test cycle, or subscription tiers) so you can choose what makes sense for your budget and project needs. And don’t forget the savings from catching issues early – every major bug found before launch can save huge costs (and reputation damage) compared to fixing it post-release.
    • Scalability and Coverage: (Bonus Benefit) Along with the above, crowdtesting inherently brings scalability and broad coverage. Want to test on 50 different device models or across 10 countries? You can scale up a crowd test to cover that, which would be infeasible for most internal teams to replicate. This elasticity means you can handle peak testing demands(say, right before a big launch or during a holiday rush) without permanently enlarging your team. And when the crunch is over, you scale down.

      The large number of testers also means you can run many test cases simultaneously, shortening the overall duration of test cycles. All of this contributes to getting high-quality products to market faster without compromising on coverage.

    By leveraging these benefits – real user insight, quick turnaround, and lower costs – companies can iterate faster and release with greater confidence.

    Check it out: We have a full article on AI-Powered User Research: Fraud, Quality & Ethical Questions

    When Should You Use Crowdsourced Testing?

    Crowdtesting can be used throughout the software development lifecycle, but there are certain scenarios where it adds especially high value. Here are a few key times to leverage global tester communities:

    Before Major Product Launches or Updates: A big product launch is high stakes – any critical bug that slips through could derail the release or sour users’ first impressions. Crowdsourced testing is an ideal pre-launch safety net. It complements your in-house QA by providing an extra round of broad, real-world testing right when it matters most. You can use the crowd to perform regression tests on new features (ensuring you didn’t break existing functionality), as well as exploratory testing to catch edge cases your team didn’t think of. The result is a smoother launch with far fewer surprises.

    By getting crowd testers to assess new areas of the application that may not have been considered by the internal QA team, you minimize the risk of a show-stopping bug on day one. In short, if a release is mission-critical, crowdtesting it beforehand can be a smart insurance policy.

    Global Rollouts and Localization: When expanding your app or service to new markets and regions, local crowdtesters are invaluable. They can verify that your product works for their locale – from language translations to regional network infrastructure and cultural expectations. Sometimes, text might not fit after translation, or an image might be inappropriate in another culture. Rather than finding out only after you’ve launched in that country, you can catch these issues early. For example, one crowdtesting case noted,

    “If you translate a phrase and the text doesn’t fit a button or if some imagery is culturally off, the crowd will find it, preventing embarrassing mistakes that could be damaging to your brand.”

    Likewise, testers across different countries can ensure your payment system works with local carriers/banks, or that your website complies with local browsers and devices. Crowdsourced testing is essentially on-demand international QA – extremely useful for global product managers.

    Ongoing Beta Programs and Early Access: If you run a beta program or staged rollout (where a feature is gradually released to a subset of users), crowdtesting can supplement these efforts. You might use a crowd community as your beta testers instead of (or in addition to) soliciting random users. The advantage is that crowdtesters are usually more organized in providing feedback and following test instructions, and you can NDA them if needed.

    Using a crowd for beta testing helps minimize risk to live users – you find and fix problems in a controlled beta environment before full release.  In practice, many companies will first roll out a new app version to crowdtesters (or a small beta group) to catch major bugs, then proceed to the app store or production once it’s stable. This approach protects your brand reputation and user experience by catching issues early.

    When You Need Specific Target Demographics or Niche Feedback: There are times you might want feedback from a very specific group – say, parents with children of a certain age testing an educational app, or users of a particular competitor product, or people in a certain profession. Crowdsourced testing platforms often allow detailed tester targeting (age, location, occupation, device type, etc.), so you can get exactly the kind of testers you need. For instance, you might recruit only enterprise IT admins to test a B2B software workflow, or only hardcore gamers to test a gaming accessory.

    The crowd platform manages finding these people for you from their large pool. This is extremely useful for user research or UX feedback from your ideal customer profile, which traditional QA teams can’t provide. Essentially, whenever you find yourself saying “I wish I could test this with [specific user type] before we go live,” that’s a cue that crowdtesting could help.

    Augmenting QA during Crunch Times: If your internal QA team is small or swamped, crowdsourced testers can offload repetitive or time-consuming tests and free your team to focus on critical areas. During crunch times – like right before a deadline or when a sudden urgent patch is needed – bringing in crowdtesters ensures nothing slips through the cracks due to lack of time. You get a burst of extra testing muscle exactly when you need it, without permanently increasing headcount.

    In summary, crowdtesting is especially useful for high-stakes releases, international launches, beta testing phases, and scaling your QA effort on demand. It’s a flexible tool in your toolkit – you might not need it for every minor update, but when the situation calls for broad, real-world coverage quickly, the crowd is hard to beat.

    Check it out: We have a full article on AI User Feedback: Improving AI Products with Human Feedback

    Choosing a Crowdsourced Testing Platform (What to Look For)

    If you’ve decided to leverage crowdsourced testing, the next step is choosing how to do it. You could try to manually recruit random testers via forums or social media, but that’s often hit-or-miss and hard to manage. The efficient approach is to use a crowdtesting platform or service that has an established community of testers and tools to manage the process.

    There are several well-known platforms in this space – including BetaTesting, Applause (uTest), Testlio, Global App Testing, Ubertesters, Testbirds, and others – each with their own strengths. Here are some key factors to consider when choosing a platform:

    • Community Size and Diversity: Look at how large and diverse the tester pool is. A bigger community (in the hundreds of thousands) means greater device coverage and faster recruiting. Diversity in geography, language, and demographics is important if you need global feedback. For instance, BetaTesting boasts a community of over 450,000 participants around the world that you can choose from. That scale can be very useful when you need lots of testers quickly or very specific targeting.

      Check if the platform can reach your target user persona – e.g., do they have testers in the right age group, country, industry, etc. Many platforms allow filtering testers by criteria like gender, age, location, device type, interests, and more.
    • Tester Quality and Vetting: Quantity is good, but quality matters too. You want a platform that ensures testers are real, reliable, and skilled. Look for services that vet their community – for example real non-anonymous, ID-verified and vetted participants. Some platforms have rating systems for testers, training programs, or certifications with smaller pools of testers.

      Read reviews or case studies to gauge if the testers on the platform tend to provide high-quality bug reports and feedback. A quick check on G2 or other review sites can reveal a lot about quality.
    • Types of Testing Supported: Consider what kinds of tests you need and whether the platform supports them. Common offerings include functional bug testing, usability testing (often via video think-alouds), beta testing over multiple days or weeks, exploratory testing, localization testing, load testing (with many users simultaneously), and more. Make sure the service you choose aligns with your test objectives. If you need moderated user interviews or very specific scenarios, check if they accommodate that.
    • Platform and Tools: A good crowdtesting platform will provide a dashboard or interface for you to define test cases, communicate with testers, and receive results (bug reports, feedback, logs, etc.) in an organized way. It should integrate with your workflow – for example, pushing bugs directly into your tracker (JIRA, Trello, etc.) and supporting attachments like screenshots or videos. Look for features like real-time reporting, automated summary of results, and perhaps AI-assisted analysis of feedback. A platform with good reporting and analytics can save you a lot of time when interpreting the test outcomes.
    • Support and Engagement Model: Different platforms offer different levels of service. Some are more self-service – you post your test and manage it yourself. Others offer managed services where a project manager helps design tests, selects testers, and ensures quality results. Decide what you need. If you’re new to crowdtesting or short on time, a managed service might be worth it (they handle the heavy lifting of coordination).

      BetaTesting, for example, provides support services that can be tailored from self-serve up to fully managed, depending on your needs. Also consider the responsiveness of the platform’s support team, and whether they provide guidance on best practices.
    • Security and NDA options: Since you might be exposing pre-release products to external people, check what confidentiality measures are in place. Reputable platforms will allow you to require NDAs with testers and have data protection measures. If you have a very sensitive application, you might choose a smaller closed group of testers (some platforms let you invite your own users into a private crowd test, for example). Always inquire about how the platform vets testers for security and handles any private data or credentials you might share during testing.
    • Pricing: Finally, consider pricing models and ensure it fits your budget. Some platforms charge per tester or per bug, others have flat fees per test cycle or subscription plans. Clarify what deliverables you get (e.g., number of testers, number of test hours, types of reports) for the price.

      While cost is important, remember to focus on value– the cheapest option may not yield the best feedback, and a slightly more expensive platform with higher quality testers could save you money by catching costly bugs early. BetaTesting and several others are known to offer flexible plans for startups, mid-size, and enterprise, so explore those options.

    It often helps to do a trial run or pilot with one platform to evaluate the results. Many companies try a small test on a couple of platforms to see which provides better bugs or insights, then standardize on one. That said, the best platform for you will depend on your specific needs and which one aligns with them.

    Check it out: We have a full article on 8 Tips for Managing Beta Testers to Avoid Headaches & Maximize Engagement

    Running Effective Crowdsourced Tests and Managing Results

    Getting the most out of crowdsourced testing requires some planning and good management. While the crowd and platform will do the heavy lifting in terms of execution, you still play a crucial role in setting the test up for successand interpreting the outcomes. Here are some tips for launching effective tests and handling the results:

    1. Define clear objectives and scope: Before you start, be crystal clear on what you want to achieve with the test. Are you looking for general bug discovery on a new feature? Do you need usability feedback on a specific flow? Is this a full regression test of an app update? Defining the scope helps you create a focused test plan and avoids wasting testers’ time. Also decide on what devices or platforms must be covered and how many testers you need for each.
    2. Communicate expectations with detailed instructions: This point cannot be overstated – clear instructions will make or break your crowdtest. Write a test plan or scenario script for the testers, explaining exactly what they should do, what aspects to focus on, and how to report issues. The more context you provide, the better the feedback.

      Once you’ve selected your testers, clearly communicating your testing requirements is crucial. Provide detailed test plans, instructions, and criteria for reporting issues. This clarity helps ensure testers know exactly what is expected of them. Don’t assume testers will intuitively know your app – give them use cases (“try to sign up, then perform X task…” etc.), but also encourage exploration beyond the script to catch unexpected bugs. It’s a balance between guidance and allowing freedom to explore. Additionally, set criteria for bug reporting (e.g. what details to include, any template or severity rating system you want).
    3. Choose the right testers: If your platform allows you to select or approve testers same as BetaTesting does, take advantage of that. You might want people from certain countries or with certain devices for particular tests. Some platforms will auto-select a broad range for you, but if it’s a niche scenario, make sure to recruit accordingly. For example, if you’re testing a fintech app, you might prefer testers with experience in finance apps.

      On managed crowdtests, discuss with the provider about the profile of testers that would be best for your project. A smaller group of highly relevant testers can often provide more valuable feedback than a large generic group.
    4. Timing and duration: Decide how long the test will run. Short “bug hunt” cycles can be 1-2 days for quick feedback. Beta tests or usability studies might run over a week or more to gather longitudinal data. Make sure testers know the timeline and any milestones (for multi-day tests, perhaps you ask for an update or a survey each day). Also be mindful of time zone differences – posting a test on Friday evening U.S. time might get faster responses from testers in Asia over the weekend, for instance. Leverage the 24/7 nature of the crowd.
    5. Engage with testers during the test: Crowdsourced doesn’t mean hands-off. Be available to answer testers’ questions or clarify instructions if something is confusing. Many platforms have a forum or chat for each test where testers can ask questions. Monitoring that can greatly improve outcomes (e.g., if multiple testers are stuck at a certain step, you might realize your instructions were unclear and issue a clarification). If you choose the BetaTesting to run, you can use our integrated message feature to communicate directly with the testers.

      This also shows testers that you’re involved, which can motivate them to provide high-quality feedback. If a tester reports something interesting but you need more info, don’t hesitate to ask them for clarification or additional details during the test cycle.
    6. Reviewing and managing results: Once the results come in (usually in the form of bug reports, feedback forms, videos, etc.), it’s time to make sense of them. This can be overwhelming if you have dozens of reports, but a good platform will help aggregate and sort them. Triage the findings: identify the critical bugs that need immediate fixing, versus minor issues or suggestions. It’s often useful to have your QA lead or a developer go through the bug list and categorize by severity.

      Many crowdtesting platforms integrate with bug tracking tools – for example, BetaTesting can push bug reports directly to Jira with all the relevant data attached, which saves manual work. Ensure each bug is well-documented and reproducible; if something isn’t clear, you can often ask the tester for more info even after they submitted (through comments). For subjective feedback (like opinions on usability), look for common themes across testers – are multiple people complaining about the registration process or a particular feature? Those are areas to prioritize for improvement.
    7. Follow up and iteration: Crowdsourced testing can be iterative. After fixing the major issues from one round, you might run a follow-up test to verify the fixes or to delve deeper into areas that had mixed feedback. This agile approach, where you test, fix, and retest, can lead to a very polished final product.

      Also, consider keeping a group of trusted crowdtesters for future (some platforms let you build a custom tester team or community for your product). They’ll become more familiar with your product over time and can be even more effective in subsequent rounds.
    8. Closing the loop: Finally, it’s good practice to close out the test by thanking the testers and perhaps providing a brief summary or resolution on the major issues. Happy testers are more likely to engage deeply in your future tests. Some companies even share with the crowd community which bugs were the most critical that they helped catch (which can be motivating).

      Remember that crowdtesters are often paid per bug or per test, so acknowledge their contributions – it’s a community and treating them well ensures high-quality participation in the long run.

    By following these best practices, you’ll maximize the value of the crowdtesting process. Essentially, treat it as a collaboration: you set them up for success, and they deliver gold in terms of user insights and bug discoveries. With your results in hand, you can proceed to launch or iterate with much greater confidence in your product’s quality.

    Challenges of Crowdsourced Testing and How to Address Them

    Crowdtesting is powerful, but it’s not without challenges. Being aware of potential pitfalls allows you to mitigate them and ensure a smooth experience. Here are some key challenges and ways to address them:

    Confidentiality and Security: Opening up your pre-release product to external testers can raise concerns about leaks or sensitive data exposure. This is a valid concern – if you’re testing a highly confidential project, crowdsourcing might feel risky. 

    How to address it: Work with platforms that take security seriously. Many platforms also allow you to test with a smaller trusted group for sensitive apps, or even invite specific users (e.g., from your company or existing customer base) into the platform environment.

    Additionally, you can limit the data shared – use dummy data or test accounts instead of real user data during the crowdtest. If the software is extremely sensitive (e.g., pre-patent intellectual property), you might hold off on crowdsourcing that portion, or only use vetted professional testers under strict contracts.

    Variable Tester Quality and Engagement: Not every crowdtester will be a rockstar; some may provide shallow feedback or even make mistakes in following instructions. There’s also the possibility of testers rushing through to maximize earnings (if paid per bug, a minority might report trivial issues to increase count). 

    How to address it: Choose a platform with good tester reputation systems and, if possible, curate your tester group (pick those with high ratings or proven expertise). Provide clear instructions to reduce misunderstandings. It can help to have a platform/project manager triage incoming reports – often they will eliminate duplicate or low-quality bug reports before you see them.

    Also, structuring incentives properly (e.g., rewarding quality of bug reports, not sheer quantity) can lead to better outcomes. Some companies run a brief pilot test with a smaller crowd and identify which testers gave the best feedback, then keep those for the main test.

    Communication Gaps: Since you’re not in the same room as the testers, clarifying issues can take longer. Testers might misinterpret something or you might find a bug report unclear and have to ask for more info asynchronously. 

    How to address it: Use the platform’s communication tools – many have a comments section on each bug or a chat for the test cycle. Engage actively and promptly; this often resolves issues. Having a dedicated coordinator or QA lead on your side to interact with testers during the test can bridge the gap. Over time, as you repeat tests, communication will improve, especially if you often work with the same crowdtesters.

    Integration with Development Cycle: If your dev team is not used to external testing, there might be initial friction in incorporating crowdtesting results. For example, developers might question the validity of a bug that only one external person found on an obscure device. 

    How to address it: Set expectations internally that crowdtesting is an extension of QA. Treat crowd-found bugs with the same seriousness as internally found ones. If a bug is hard to reproduce, you can often ask the tester for additional details or attempt to reproduce via an internal emulator or device lab. Integrate the crowdtesting cycle into your sprints – e.g., schedule a crowdtest right after code freeze, so developers know to expect a batch of issues to fix. Making it part of the regular development rhythm helps avoid any perception of “random” outside input.

    Potential for Too Many Reports: Sometimes, especially with a large tester group, you might get hundreds of feedback items. While in general more feedback is better than less, it can be overwhelming to process. 

    How to address it: Plan for triage. Use tags or categories to sort bugs (many platforms let testers categorize bug types or severity). Have multiple team members review portions of the reports. If you get a lot of duplicate feedback (which can happen with usability opinions), that actually helps you gauge impact – frequent mentions mean it’s probably important. Leverage any tools the platform provides for summarizing results. For instance, some might give you a summary report highlighting the top issues. You can also ask the platform’s project manager to provide an executive summary if available.

    Not a Silver Bullet for All Testing: Crowdtesting is fantastic for finding functional bugs and getting broad feedback, but it might not replace specialized testing like deep performance tuning, extensive security penetration testing, or very domain-specific test cases that require internal knowledge. 

    How to address it: Use crowdtesting in conjunction with other QA methods. For example, you might use automation for performance tests, or have security experts for a security audit, and use crowdtesting for what it excels at (real user scenarios, device diversity, etc.). Understand its limits: if your app requires knowledge of internal algorithms or access to source code to test certain things, crowdsourced testers won’t have that context. Mitigate this by pairing crowd tests with an internal engineer who can run complementary tests in those areas.

    The good news is that many of these challenges can be managed with careful planning and the right partner. As with any approach, learning and refining your process will make crowdtesting smoother each time. Many companies have successfully integrated crowdtesting by establishing clear protocols – for instance, requiring all testers to sign NDAs, using vetted pools of testers for each product line, and scheduling regular communication checkpoints.

    By addressing concerns around confidentiality, reliability, and coordination (often with help from the platform itself), you can reap the benefits of the crowd while minimizing downsides. Remember that crowdtesting has been used by very security-conscious organizations as well – even banking and fintech companies – by employing best practices like NDA-bound invitation-only crowds. So the challenges are surmountable with the right strategy.

    Final Thoughts

    Crowdsourced testing is a powerful approach to quality assurance that, when used thoughtfully, can significantly enhance product quality and user satisfaction. It matters because it injects real-world perspective into the testing process, something increasingly important as products reach global and diverse audiences.

    Crowdtesting differs from traditional QA in its scalability, speed, breadth, offering benefits like authentic feedback, rapid results, and cost efficiency. It’s particularly useful at critical junctures like launches or expansions, and with the right platform (such as BetaTesting.com and others) and best practices, it can be seamlessly integrated into a team’s workflow. Challenges like security and communication can be managed with proper planning, as demonstrated by the many organizations successfully using crowdtesting today.

    For product managers, engineers, and entrepreneurs, the takeaway is that you’re not alone in the quest for quality – there’s a whole world of testers out there ready to help make your product better. Leveraging that global tester community can be the difference between a flop and a flawless user experience.

    As you plan your next product cycle, consider where “the power of the crowd” might give you the edge in QA. You might find that it not only improves your product, but also provides fresh insights and inspiration that elevate your team’s perspective on how real users interact with your creation. And ultimately, building products that real users love is what crowd testing is all about.

    Have questions? Book a call in our call calendar.

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  • Global App Testing: Testing Your App, Software or Hardware Globally

    Why Does Global App Testing Matter?

    In today’s interconnected world, most software and hardware products are ultimately destined for global distribution. But frequently, these products are only tested in the lab or in the country in which it was manufactured, leading to bad user experiences, poor sales, and failed marketing campaigns.

    How do you solve this? With global app testing and product testing. Put your app, website, or physical product (e.g. TVs, streaming media devices, vacuums, etc) in the hands of users in each country it’s meant to be distributed.

    If you plan to launch your product globally (now or in the future), you need feedback and testing from around the world to ensure your product is technically stable and provides a great user experience.

    Here’s what we will explore:

    1. Why Does Global App Testing Matter?
    2. How to Find and Recruit the Right Testers
    3. How to Handle Logistics and Communication Across Borders
    4. Let the Global Insights Shape Your Product

    The benefits of having testers from multiple countries and cultures are vast:

    • Diverse Perspectives Uncover More Issues: Testers in different regions can reveal unique bugs and usability issues that stem from local conditions, whether it’s language translations breaking the UI, text rendering with unique languages, or payment workflows failing on a country-specific gateway. In other words, a global app testing pool helps ensure your app works for “everyone, everywhere.”
    • Cultural Insights Drive Better UX: Beyond technical bugs, global testers provide culturally relevant feedback. They might highlight if a feature is culturally inappropriate or if content doesn’t make sense in their context. Research shows that digital products built only for a local profile often flop abroad, simply because a design that succeeds at home can confuse users from a different culture.

      By beta testing internationally, you gather insights to adapt your product’s language, design, and features to each culture’s expectations. For example, a color or icon that appeals in one culture may carry a negative meaning in another; your global testers will call this out so you can adjust early.
    • Confidence in Global Readiness: Perhaps the biggest payoff of global beta testing is confidence. Knowing that real users on every continent have vetted your app means fewer nasty surprises at launch. You can be sure that your e-commerce site handles European privacy prompts correctly, your game’s servers hold up in Southeast Asia, or that your smart home device complies with voltage standards and user habits in each country. It’s far better to find and fix these issues in a controlled beta than after a worldwide rollout.

    That said, you don’t need to test in every country on the planet. 

    Choosing the right regions is key. Focus on areas aligned with your target audience and growth plans. Use data-driven tools (like Google’s Market Finder) to identify high-potential markets based on factors like mobile usage, revenue opportunities, popular payment methods, and localization requirements. For instance, if Southeast Asia or South America show a surge in users interested in your product category, those regions might be prime beta locales.

    Also, look at where you’re already getting traction. If you’ve released a soft launch or have early analytics, examine whether people in certain countries are already installing or talking about your app. If so, that market likely deserves inclusion in your beta. Google’s experts suggest checking if users in a region are already installing your app, using it, leaving feedback and talking about it on social media as a signal of where to focus. In practice, if you notice a spike of sign-ups from Brazil or discussions about your product on a German forum, consider running beta tests there, these engaged users can give invaluable localized feedback and potentially become your advocates.

    In summary, global app testing matters because it ensures your product is truly ready for a worldwide audience. It leverages the power of diversity, in culture, language, and tech environments to polish your app or device. You’ll catch region-specific issues, learn what delights or frustrates users in each market, and build a blueprint for a successful global launch. In the next sections, we’ll explore how to actually recruit those international testers and manage the logistics of testing across borders.

    Check it out: We have a full article on AI Product Validation With Beta Testing

    How to Find and Recruit the Right Testers Around the World

    Sourcing testers from around the world might sound daunting, but today there are many avenues to find them. The goal is to recruit people who closely resemble your target customers in each region, not just random crowds, but real users who fit your criteria. Here are some effective strategies to find and engage quality global testers:

    • Leverage beta testing platforms: Dedicated beta testing services like BetaTesting and similar platforms maintain large communities of global testers eager to try new products. For example, BetaTesting’s platform boasts a network of over 450,000 real-world participants across diverse demographics and over 200 countries, so teams can easily recruit testers that match their target audience.

      These platforms often handle a lot of heavy lifting, from participant onboarding to feedback collection, making it simpler to run a worldwide test. As a product manager, you can specify the countries, devices, or user profiles you need, and the platform will find suitable candidates. Beta platforms can give you fast access to an international pool.
    • Tap into online communities: Outside of official platforms, online communities and forums are fertile ground for finding enthusiastic beta testers worldwide. Think Reddit (which has subreddits for beta testing and country-specific communities), tech forums, Discord groups, or product enthusiast communities. A creative post or targeted ad campaign in regions you’re targeting can attract users who are interested in your domain (for example, posting in a German Android fan Facebook group if you need Android testers in Germany). Be sure to clearly explain the opportunity and any incentives (e.g. “Help us test our new app, get early access and a $20 gift card for your feedback”).

      Additionally, consider communities like BetaTesting’s own (they invite tech-savvy consumers to sign up as beta testers) where thousands of users sign up for testing opportunities. These communities often have built-in geo-targeting, you can request, say, 50 testers in Europe and 50 in Asia, and the community managers will handle the outreach.
    • Recruit from your user base: If you already have users or an email list in multiple countries (perhaps for an existing product or a previous campaign), don’t overlook them. In-app or in-product invitations can be highly effective because those people are already interested in your brand. For example, you might add a banner in your app or website for users in Canada and India saying, “We’re launching something new, sign up for our global beta program!” Often, your current users will be excited to join a beta for early access or exclusive benefits. Plus, they’ll provide very relevant feedback since they’re already somewhat familiar with your product ecosystem. (Just be mindful of not cannibalizing your production usage, make sure it’s clear what the beta is and perhaps target power-users who love giving feedback.)

    No matter which recruitment channels you use, screening and selecting the right testers is crucial. You’ll want to use geotargeting and screening surveys to pinpoint testers who meet your criteria. This is especially important when going global, where you may have specific requirements for each region. For instance, imagine you need testers in Japan who use iOS 16+, or gamers in France on a particular console, or families in Brazil with a smart home setup.

    Craft a screener survey that filters for those attributes (e.g. “What smartphone do you use? answer must be iPhone; What country do you reside in? must be Japan”). Many beta platforms provide advanced filtering tools to do this automatically. BetaTesting, for example, allows clients to filter and select testers based on hundreds of targeting criteria, from basics like age, gender, and location, to specifics like technology usage, hobbies, or profession. Use these tools or your own surveys to ensure you’re recruiting ideal testers (not just anybody with an internet connection).

    Also, coordinate the distribution of testers across devices and networks that matter to you. If your app is used on both low-end and high-end phones, or in both urban high-speed internet and rural 3G conditions, aim to include that variety in your beta pool. In the global context, this means if you’re testing a mobile app, try to get a spread of iPhones and Android models common in each country (remember that in some markets budget Android devices dominate, whereas in others many use the latest iPhone).

    Likewise, consider telecom networks, a beta for a streaming app might include testers on various carriers or internet speeds in each country to see how the experience holds up. Coordinating this distribution will give you confidence that your product performs well across the spectrum of devices, OS versions, and network conditions encountered globally.

    Finally, provide a fair incentive for participation. To recruit high-quality testers, especially busy professionals or niche users, you need to respect their time and effort. While some superfans might test for free, most formal global beta tests include a reward (monetary payments, gift cards, discounts, or exclusive perks are common).

    Offering reasonable incentives not only boosts sign-ups but also leads to more thoughtful feedback, as people feel their contribution is valued. On the flip side, being too stingy can backfire; you might only attract those looking for a quick payout rather than genuine testers. 

    In practice, consider the cost of living and typical income levels in each country when setting incentives. An amount that is motivating in one region might be trivial in another (or vice versa). When recruiting globally, “meaningful” might vary, e.g. $15 Amazon US gift card for a short test might be fine in the US, but you might choose a different voucher of equivalent value for testers in India or Nigeria. The key is to make it fair and culturally appropriate (some may prefer cash via PayPal or bank transfer, others might be happy with a local e-commerce gift card). We’ll discuss the logistics of distributing these incentives across borders next, which is its own challenge.

    Check it out: We have a full article on Giving Incentives for Beta Testing & User Research

    How to Handle Logistics and Communication Across Borders

    Running a global beta test isn’t just about finding testers, you also have to manage the logistics and communication so that the experience is smooth for both you and the participants. Different time zones, languages, payment systems, and even shipping regulations can complicate matters. With some planning and the right tools, however, you can overcome these hurdles. Let’s break down the main considerations:

    Incentives and Reward Payments Across Countries

    Planning how to deliver incentives or rewards internationally is one of the trickiest aspects of global testing. As noted, it’s standard to compensate beta testers (often with money or gift cards), but paying people in dozens of countries is not as simple as paying your neighbor. For one, not every country supports PayPal, the go-to payment method for many online projects. In fact, PayPal is unavailable in 28 countries as of recent counts, including sizable markets like Bangladesh, Pakistan, and Iran among others.

    Even where PayPal is available, testers may face high fees, setup hassles (e.g. difficult business paperwork required) or other issues. Other payment methods have their own regional limitations and regulations (for example, some countries restrict international bank transfers or require specific tax documentation for foreign payments).

    The prospect of figuring out a unique payment solution for each country can be overwhelming, and you probably don’t want to spend weeks navigating foreign banking systems. The good news is you don’t have to reinvent the wheel. We recommend using a provider like Tremendous (or similar global reward platforms) to facilitate reward distribution throughout the globe.

    What’s the solution? A global reward distribution platform. Platforms like Tremendous specialize in this: you fund a single account and they can send out rewards that are redeemable as gift cards, prepaid Visa cards, PayPal funds, or other local options to recipients in over 200 countries with just a few clicks. They also handle currency conversions and compliance, sparing you a lot of headaches. The benefit is two-fold: you ensure testers everywhere actually receive their reward in a usable form, and you save massive administrative time.

    Using a global incentive platform can dramatically streamline cross-border payments. The takeaway: a single integrated rewards platform lets you treat your global testers fairly and equally, without worrying about who can or cannot receive a PayPal payment. It’s a one-stop solution, you set the reward amount for each tester, and the platform handles delivering it in a form that works in their country.

    A few additional tips on incentives: Be transparent with testers about what reward they’ll get and when. Provide estimated timelines (e.g. “within 1 week of test completion”) and honor them, prompt payment helps build trust and keeps testers motivated. Also, consider using digital rewards (e.g. e-gift codes) which are easier across borders than physical items.

    And finally, keep an eye on fraud; unfortunately, incentives can attract opportunists. Requiring testers to verify identity or using a platform that flags suspicious behavior (Tremendous, for instance, has fraud checks built-in) will ensure you’re rewarding genuine participants only.

    Multilingual Communication and Support

    When testers are spread across countries, language becomes a key factor in effective communication. To get quality feedback, participants need to fully understand your instructions, and you need to understand their feedback. The best practice is to provide all study materials in each tester’s local language whenever possible.

    In countries where English isn’t the official language, you should translate your test instructions, tasks, and questions into the local tongue. Otherwise, you’ll drastically shrink the pool of people who can participate and risk getting poor data because testers struggle with a foreign language. For example, if you run a test in Spain, conduct it in Spanish, an English-only test in Spain would exclude many willing testers and impact the data quality and study results..

    On the feedback side, consider allowing testers to respond in their native language, too. Not everyone is comfortable writing long-form opinions in English, and you might get more nuanced insights if they can express themselves freely. You can always translate their responses after (either through services or modern AI translation tools which have gotten quite good).

    If running a moderated test (like live interviews or focus groups) in another language, hire interpreters or bilingual moderators. A local facilitator who speaks the language can engage with testers smoothly and catch cultural subtleties that an outsider might miss. This not only removes language barriers but also puts participants at ease, they’re likely to open up more to someone who understands their norms and can probe in culturally appropriate ways.

    For documentation, translate any key communications like welcome messages, instructions, and surveys. However, also maintain an English master copy internally so you can aggregate findings later. It’s helpful to have a native speaker review translations to avoid any awkward phrasing that could confuse testers.

    During the test, be ready to offer multilingual support: if a tester emails with a question in French, have someone who can respond in French (or use a translation tool carefully). Even simple things like providing customer support contacts or FAQs in the local language can significantly improve the tester experience.

    Another strategy for complex, multi-country projects is to appoint local project managers or coordinators for each region. This could be an employee or a partner who is on the ground, speaks the language, and knows the culture. They can handle on-the-spot issues, moderate discussions, and generally “translate” both language and cultural context between your central team and the local testers.

    For a multi-week beta or a hardware trial, a local coordinator can arrange things like shipping (as we’ll discuss next) and even host meet-ups or Q&A sessions in the local language. While it adds a bit of cost, it can drastically increase participant engagement and the richness of feedback, plus it shows respect to your testers that you invested in local support.

    Shipping Physical Products Internationally

    If you’re beta testing a physical product (say a gadget, IoT device, or any hardware), logistics get even more tangible: you need to get the product into testers’ hands across borders. Shipping hardware around the world comes with challenges like customs, import fees, longer transit times, and potential damage or loss in transit. Based on hard-earned experience, here are some tips to manage global shipping for a beta program:

    Ship from within each country if possible: If you have inventory available, try to dispatch products from a local warehouse or office in each target country/region. Domestic shipping is far simpler (no customs forms, minimal delays) and often cheaper. If you’re a large company with international warehouses, leverage them. If not, an alternative is the “hub and spoke” approach, bulk ship a batch of units to a trusted partner or team member within the region, and then have them forward individual units to testers in that country.

    For example, you could send one big box or pallet of devices to your team in France, who then distributes the packages locally to the testers in France. This avoids each tester’s package being stuck at customs or incurring separate import taxes when shipping packages individually.

    Use proven, high-quality shipping companies: We recommend using proven shipping services for overseas shipping (e..g think FedEx, DHL, UPS, GLS, DPD, etc). We also recommend using the fastest shipping method that is affordable. Most of these companies greatly simplify the complexity of dealing with international shipping regulations and customs definitions.

    Mind customs and regulations: When dealing with customs paperwork, do your homework on import rules and requirements and be sure to complete all the paperwork properly (this is where it helps to work with proven international shipping companies). Be sure when creating your shipment that you are paying for any import fees and the cost of shipping directly to your testers door. If your testers are required to pay out of pocket for duties / taxes / customs charges, you are going to run into major logistical issues.

    Provide tracking and communicate proactively: Assign each shipment a tracking number and share it with the respective tester (along with the courier site to track). Ideally, also link each tester’s email or phone to the shipment so the courier can send them updates directly. This way, testers know when to expect the package and can retrieve it if delivery is attempted when they’re out.

    Having tracking also gives you oversight; you can see if a package is delayed or stuck and intervene. Create a simple spreadsheet or use your beta platform to map which tester got which tracking number, this will be invaluable if something goes awry.

    Plan for returns (if needed): Decide upfront whether you need the products back at the end of testing. If yes, tell testers before they join that return shipping will be required after the beta period. Testers are usually fine with this as long as it’s clear and easy. To make returns painless, include a prepaid return shipping label in the box or send them one via email later. Arrange pickups if possible or instruct testers how to drop off the package.

    Using major international carriers like FedEx, DHL, or UPS can simplify return logistics, they have reliable cross-border services and you can often manage return labels from your home country account. If devices aren’t being returned (common for cheaper items or as an added incentive), be explicit that testers can keep the product, they’ll love that!

    Have a backup plan for lost/damaged units: International shipping has risks, so factor in a few extra units beyond the number of testers, in case a package is lost or a device arrives broken. You don’t want a valuable tester in Australia to be empty-handed because their device got stuck in transit. If a delay or loss happens, communicate quickly with the tester, apologize, and ship a replacement if possible. Testers will understand issues, but they appreciate prompt and honest communication.

    By handling the shipping logistics thoughtfully, you ensure that physical product testing across regions goes as smoothly as possible. Some beta platforms (like BetaTesting) can also assist or advise on logistics if needed, since we’ve managed projects shipping products globally. The core idea is to minimize the burden on testers, they should spend their time testing and giving feedback, not dealing with shipping bureaucracy.

    Check it out: Top 10 AI Terms Startups Need to Know

    Coordinating Across Time Zones

    Time zones are an inevitable puzzle in global testing. Your testers might be spread from California to Cairo to Kolkata, how do you coordinate schedules, especially if your test involves any real-time events or deadlines? The key is flexibility and careful scheduling to accommodate different local times.

    First, if your beta tasks are asynchronous (e.g. complete a list of tasks at your convenience over a week), then time zones aren’t a huge issue beyond setting a reasonable overall schedule. Just be mindful to set deadlines in a way that is fair to all regions. If you say “submit feedback by July 10 at 5:00 PM,” specify the time zone (and perhaps translate it: e.g. “5:00 PM GMT+0, which is 6:00 PM in London, 1:00 PM in New York, 10:30 PM in New Delhi,” etc.). Better yet, use a tool that localizes deadlines for each user or just give a date and allow the end of that date in each tester’s time zone. The goal is to avoid a scenario where it’s July 11 morning for half your testers when it’s still July 10 for you, that can cause confusion or people missing the cutoff. A simple solution is to pick a deadline that effectively gives everyone the same amount of time, or explicitly state different deadlines per region (“submit by 6 PM your local time on July 10”).

    If your test involves synchronous activities, say a scheduled webinar, a multiplayer game session, or a live interview, then you’ll need to plan with time zones in mind. You likely won’t find one time that’s convenient for everyone (world night owls are rare!). One approach is to schedule multiple sessions at different times to cover groups of time zones.

    For example, host one live gameplay session targeting Americas/Europe time, and another for Asia/Pacific time. This way, each tester can join during their daytime rather than at 3 AM. It’s unrealistic to expect, for instance, UK testers to participate in an activity timed for a US evening. As an example, if you need a stress test of a server at a specific moment, you might coordinate “waves” of testers: one wave at 9 PM London time and another at 9 PM New York time, etc. While it splits the crowd, it’s better than poor engagement because half the testers were asleep.

    For general communication, stagger your messages or support availability to match business hours in different regions. If you send an important instruction email, consider that your Australian testers might see it 12 hours before your American testers due to time differences. It can be helpful to use scheduling tools or just time your communications in batches (e.g. send one batch of emails in the morning GMT for Europeans/Asians and another batch later for Americas). Also, beware of idiomatic time references, saying “we’ll regroup tomorrow” in a message can confuse if it’s already tomorrow in another region. Always clarify dates with the month/day to avoid ambiguity.

    Interestingly, having testers across time zones can be an advantage for quickly iterating on feedback. When you coordinate properly, you could receive test results almost 24/7. Essentially, while your U.S. testers sleep, your Asian testers might be busy finding bugs, and vice versa, giving you continuous coverage. To harness this, you can review feedback each morning from one part of the world and make adjustments that another group of testers will see as they begin their day. It’s like following the sun.

    To efficiently track engagement and progress, use a centralized tool (like your beta platform or even a shared dashboard) that shows who has completed which tasks, regardless of time zone. That way, you’re not manually calculating time differences to figure out if Tester X in Australia is actually late or not. Many platforms timestamp submissions in UTC or your local time, so be cautious interpreting them, know what baseline is being used. If needed, just communicate and clarify with testers if you see someone lagging; it might be a time confusion rather than lack of commitment.

    In summary, be timezone-aware in every aspect: scheduling, communications, and expectation setting. Plan in a way that respects local times, your testers will appreciate it and you’ll get better participation. And if you ever find yourself puzzled by a time zone, tools like world clocks or meeting planners are your friend (there are many online services where you plug in cities and get a nice comparison chart). After a couple of global tests, you’ll start memorizing time offsets (“Oh, 10 AM in San Francisco is 6 PM in London, which is 1 AM in Beijing, maybe not ideal for China”). It’s a learning curve but very doable.

    Handling International Data Privacy and Compliance

    Last but certainly not least, data privacy and legal compliance must be considered when running tests across countries. Each region may have its own laws governing user data, personal information, and how it can be collected or transferred. When you invite beta testers, you are essentially collecting personal data (names, emails, maybe usage data or survey answers), so you need to ensure you comply with regulations like Europe’s GDPR, California’s CCPA, and others as applicable.

    The general rule is: follow the strictest applicable laws for any given tester. For example, if you have even a single tester from the EU, the General Data Protection Regulation (GDPR) applies to their data, regardless of where your company is located. GDPR is one of the world’s most robust privacy laws, and non-compliance can lead to hefty fines (up to 4% of global revenue or €20 million).

    So if you’re US-based but testing with EU citizens, you must treat their data per GDPR standards: obtain clear consent for data collection, explain how the data will be used, allow them to request deletion of their data, and secure the data properly. Similarly, if you have testers in California, the CCPA gives them rights like opting out of the sale of personal info, etc., which you should honor.

    What does this mean in practice? Informed consent is paramount. When recruiting testers, provide them with a consent form or agreement that outlines what data you’ll collect (e.g. “We will record your screen during testing” or “We will collect usage logs from the device”), how you will use it, and that by participating they agree to this. Make sure this complies with local requirements (for instance, GDPR requires explicit opt-in consent and the ability to withdraw consent). It’s wise to have a standard beta tester agreement that includes confidentiality (to protect your IP) and privacy clauses. All testers should sign or agree to this before starting. Many companies use electronic click-wrap agreements on their beta signup page.

    Data handling is another aspect: ensure any personal data from testers is stored securely and only accessible to those who need it. If you’re using a beta platform, check that they are GDPR-compliant and ideally have things like EU-US Privacy Shield or Standard Contractual Clauses in place if data is moving internationally. If you’re managing data yourself, consider storing EU tester data on EU servers, or at least use reputable cloud services with strong security.

    Additionally, ask yourself if you really need each piece of personal data you collect. Minimization is a good principle, don’t collect extra identifiable info unless it’s useful for the test. For example, you might need a tester’s phone number for shipping a device or scheduling an interview, but you probably don’t need their full home address if it’s a purely digital test. Whatever data you do collect, only use it for the purposes of the beta test and then dispose of it safely when it’s no longer needed.

    Be mindful of special data regulations in various countries. Some countries have data residency rules (e.g. Russia requires that citizens’ personal data be stored on servers within Russia). If you happen to have testers from such countries, consult legal advice on compliance or avoid collecting highly sensitive data. Also, if your beta involves collecting user-generated content (like videos of testers using the product), get explicit permission to use that data for research. Typically, a clause in the consent that any feedback or content they provide can be used by your company internally for product improvement is sufficient.

    One often overlooked aspect is NDAs and confidentiality from the tester side. While it’s not exactly a privacy law, you’ll likely want testers to keep the beta product and their feedback confidential (to prevent leaks of your features or intellectual property).

    Include a non-disclosure agreement in your terms so that testers agree not to share information about the beta outside of authorized channels. Most genuine testers are happy to comply, they understand they’re seeing pre-release material. Reinforce this by marking communications “Confidential” and perhaps setting up a private forum or feedback tool that isn’t publicly visible.

    In summary, treat tester data with the same care as you would any customer data, if not more, since beta programs sometimes collect more detailed usage info. When in doubt, consult your legal team or privacy experts to ensure you have all the needed consent and data protections in place. It may seem like extra paperwork, but it’s critical. With the legalities handled, you can proceed to actually use those global insights to improve your product.

    Let the Global Insights Shape Your Product

    After executing a global beta test, recruiting diverse users, collecting their feedback, and managing the logistics you’ll end up with a treasure trove of insights. Now it’s time to put those insights to work. The ultimate goal of any beta is to learn and improve the product before the big launch (and even post-launch for continuous improvement).

    When your beta spans multiple countries and cultures, the learnings can be incredibly rich and sometimes surprising. Embracing these global insights will help you adapt your product, marketing, and strategy for success across diverse user groups.

    First, aggregate and analyze the feedback by region and culture. Look for both universal trends and local differences. You might find that users everywhere loved Feature A but struggled with Feature B, that’s a clear mandate to fix Feature B for all. But you may also discover that what one group of users says doesn’t hold true for another group.

    For example, your beta feedback might reveal that U.S. testers find your app’s signup process easy, while many Japanese testers found it confusing (perhaps due to language nuances or different UX expectations). Such contrasts are gold: they allow you to decide whether to implement region-specific changes or a one-size-fits-all improvement. You’re essentially pinpointing exactly what each segment of users needs.

    Use these insights to drive product adaptations. Is there a feature you need to tweak for cultural relevance? For instance, maybe your social app had an “avatar” feature that Western users enjoyed, but in some Asian countries testers expected more privacy and disliked it. You might then make that feature optional or change its default settings in those regions. Or let’s say your e-commerce beta revealed that Indian users strongly prefer cash-on-delivery option, whereas U.S. users are fine with credit cards, you’d want to ensure your payment options at launch reflect that. 

    Global betas also highlight logistical or operational challenges you might face during a full launch. Pay attention to any hiccups that occurred during the test coordination: did testers in one country consistently have trouble connecting to your server? That might indicate you need a closer server node or CDN in that region before launch. Did shipping hardware to a particular country get delayed excessively? That could mean you should set up longer lead times or a local distributor there.

    Perhaps your support team got a lot of questions from one locale, maybe you need a FAQ in that language or a support rep who speaks it. Treat the beta as a rehearsal not just for the product but for all surrounding operations. By solving these in beta, you pave the way for a smoother public rollout in each region.

    Now, how do you measure success across diverse user groups? In a global test, success may look different in different places. It’s important to define key metrics for each segment. For instance, you might measure task completion rates, satisfaction scores, or performance benchmarks separately for Europe, Asia, etc., then compare. The goal is not to pit regions against each other, but to ensure that each one meets an acceptable threshold. If one country’s testers had a 50% task failure rate while others were 90% successful, that’s a red flag to investigate. It could be a localization bug or a fundamentally different user expectation. By segmenting your beta data, you avoid a pitfall of averaging everything together and missing outlier problems. A successful beta outcome is when each target region shows positive indicators that the product meets users’ needs.

    Another way to leverage global beta insights is in your marketing and positioning for launch. Your testers’ feedback tells you what value propositions resonate with different audiences. Perhaps testers in Latin America kept praising your app’s offline functionality (due to spottier internet), while testers in Scandinavia loved the security features. Those are clues to highlight different messaging in those markets’ marketing campaigns. You can even gather testimonials or quotes from enthusiastic beta users around the world (with their permission) to use as social proof in regional marketing. Early adopters’ voices, especially from within a market, can greatly boost credibility when you launch widely.

    One concrete example: Eero, a mesh WiFi startup, ran an extensive beta with users across various home environments. By ensuring a “very diverse representation” of their customer base in the beta, they were able to identify and fix major issues before the official launch.

    They chose testers with different house sizes, layouts, and ISP providers to mirror the breadth of real customers. This meant that when Eero launched, they were confident the product would perform well whether in a small city apartment or a large rural home. That beta-driven refinement led to glowing reviews and a smooth rollout, the diverse insights literally shaped a better product and a winning launch.

    Finally, keep iterating. Global testing is not a one-and-done if your product will continue to evolve. Leverage beta insights to shape not just the launch version, but your long-term roadmap. Some features requested by testers in one region might be scheduled for a later update or a region-specific edition. You might even decide to do follow-up betas or A/B tests targeted at certain countries as you fine-tune. The learnings from this global beta can inform your product development for years, especially as you expand into new markets.

    Crucially, share the insights with your whole team, product designers, engineers, marketers, executives. It helps build a global mindset internally. When an engineer sees feedback like “Users in country X all struggled with the sign-up flow because the phone number formatting was unfamiliar,” it creates empathy and understanding that design can’t be U.S.-centric (for instance). When a marketer hears that “Testers in country Y didn’t understand the feature until we described it in this way,” they can adjust the messaging in that locale.

    Check it out: We have a full article on AI in User Research & Testing in 2025: The State of The Industry

    Conclusion

    Global app testing provides the multi-cultural, real-world input that can elevate your product from good to great on the world stage. By thoughtfully recruiting international testers, handling the cross-border logistics, and truly listening to the feedback from each region, you equip yourself with the knowledge to launch and grow your product worldwide.

    The insights you gain, whether it’s a minor UI tweak or a major feature pivot will help ensure that when users from New York to New Delhi to New South Wales try your product, it feels like it was made for them. And in a sense, it was, because their voices helped shape it.

    Global beta testing isn’t always easy, but the payoff is a product that can confidently cross borders and an organization that learns how to operate globally. By following the strategies outlined, from incentive planning to localizing communication to embracing culturally diverse feedback, you can navigate the challenges and reap the rewards of testing all around the world. So go ahead and take your product into the wild worldwide; with proper preparation and openness to learn, the global insights will guide you to success.

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  • How to Get Humans for AI Feedback

    Why the Right Audience Matters for AI Feedback

    AI models, especially large language models (LLMs) used for chatbots and a host of other modern AI functionality, learn and improve through human feedback. But the feedback you use to evaluate and fine-tune your AI models greatly influences how useful your models and agents become. It’s crucial to recruit participants for AI feedback who truly represent the end-users or have the domain expertise that is needed to improve your model.

    As one testing guide from Poll the People puts it: 

    “You should always test with people who are in your target audience. This ensures you’re getting accurate feedback about your product or service.” 

    In other words, to get feedback to fine-tune an AI model or functional product that is designed to provide financial advice, you should rely on experts that are qualified to give feedback on such a product, for example financial professionals or retail (consumer) investors. If you’re relying on the foundational model or a model that was fine-tuned using your average joe schmo, it’s probably not going to be provide great results!

    Here’s what we will explore:

    1. Why the Right Audience Matters for AI Feedback?
    2. From Foundation Models to Expert-Tuned AI
    3. Strategies to Recruit the Right People for AI Feedback

    Using the wrong audience for AI feedback can lead to misleading or low-value output. For example, testing a specialized medical chatbot on random laypersons might yield feedback about its general grammar or interface, but miss crucial medical inaccuracies that a doctor would catch. Similarly, an AI coding assistant evaluated only by novice programmers might appear fine, while seasoned software engineers would expose its deeper shortcomings.

    Relying solely on eager but non-representative beta users can result in a very generic clump of usage and bug reports while overlooking some more nuanced aspects of the user experience that your target audience might care about. In short, the quality of AI feedback is only as good as the humans who provide it.

    The recent success of reinforcement learning from human feedback (RLHF) in training models like ChatGPT underscores the importance of having the right people in the loop. RLHF works by having humans rank or score AI outputs and using those preferences to fine-tune the model. If those human raters don’t understand the domain or user needs, their feedback could optimize the AI in the wrong direction.

    To truly align AI behavior with what users want and expect, we need feedback from users who mirror the intended audience and experts who can judge accuracy in specialized tasks.

    Check it out: See our posts on Improving AI Products with Human Feedback and RLHF

    From Foundation Models to Expert-Tuned AI

    Many of today’s foundation models (the big general-purpose AIs) were initially trained on vast data from the internet or crowdsourced annotationd, not exclusively by domain experts. For instance, most LLMs (like OpenAI’s) are primarily trained on internet text and later improved via human feedback provided by contracted crowd workers. These low paid taskers may be skilled at the technical details of labeling / annotating, but almost certainly they are not experts in much of the content they are actually labeling.

    This broad non-expert training is one reason these models can sometimes produce incorrect medical or legal advice: the model wasn’t built with expert-only data and it wasn’t evaluated and fine-tuned with expert feedback. In short, general AI models often lack specialized expertise because they weren’t trained by specialists.

    To unlock higher accuracy and utility in specific domains, AI engineers have learned that models require evaluation and fine-tuning with expert audiences. By recruiting actual software developers to provide examples and feedback, the AI could learn to generate code at a higher quality.

    An example comes from the medical AI realm. Google’s Med-PaLM 2, a large language model for medicine, was fine-tuned and evaluated with the help of medical professionals. In fact, the model’s answers were evaluated by human raters, both physicians and lay people to ensure clinical relevance and safety. In that evaluation, doctors rated the AI’s answers as comparable in quality to answers from other clinicians on most axes, a result only achievable by involving experts in the training and feedback loop.

    Recognizing this need, new services have emerged to connect AI projects with subject-matter specialists. For instance, Pareto.AI focuses on expert labeling. The premise is that an AI can be taught or evaluated by people who deeply understand the content, be it doctors, lawyers, financial analysts, or specific consumers (for consumer products). This expert-driven approach can significantly improve an AI’s performance in specialized tasks, from diagnosing medical images to interpreting legal documents. Domain experts ensure that fine-tuning aligns with industry standards and real-world accuracy, rather than just general internet.

    The bottom line is that while foundation models give us powerful general intelligence, human feedback from the right humans is what turns that general ability into expert performance. Whether it’s via formal RLHF programs or informal beta tests, getting qualified people to train, test, and refine AI systems is often the secret sauce behind the best AI products.

    Check it out: Top 10 AI Terms Startups Need to Know

    Strategies to Recruit the Right People for AI Feedback

    So how can teams building AI products, especially generative AI like chatbots or LLM-powered apps, recruit the right humans to provide feedback? Below are key strategies and channels to find and engage the ideal participants for your AI testing and training efforts.

    1. Tapping Internal Talent and Loyal Users (Internal Recruitment)

    One immediate resource is within your own walls. Internal beta testing (sometimes called “dogfooding”) involves using your company’s employees or existing close customers to test AI products early. Employees can be great guinea pigs for an AI chatbot, since they’re readily available and already understand the product vision.

    Many organizations run “alpha tests” internally before any external release. This helps catch obvious bugs or alignment issues. For example, during internal tests at Google, employees famously tried early versions of AI models like Google Assistant and provided feedback before public rollout. However, be mindful of the limitations of internal testing. By nature, employees are not fully representative of your target market, and they might be biased or hesitant to give frank criticism. 

    Internal recruitment can extend beyond employees to a trusted circle of power users or early adopters of your product. These could be customers who have shown enthusiasm for your company and volunteered to try new features. Such insiders are often invested in your success and will gladly spend time giving detailed feedback.

    In the context of AI, if you’re developing, say, an AI design assistant, your long-time users in design roles could be invited to an early access program to critique the AI’s suggestions. They bring both a user’s perspective and a bit of domain expertise, acting as a bridge before you open testing to the wider world.

    Overall, leveraging internal and close-known users is a low-cost, quick way to get initial human feedback for your AI. Just remember to diversify beyond the office when you move into serious beta testing, so you don’t fall into the trap of insular feedback.

    2. Reaching Out via Social Media and Communities

    The internet can be your ally when seeking humans to test AI (but of course beware of fraud, as there is a lot out there).

    You can find people in their natural digital habitats who match your target profile. Social media, forums, and online communities are excellent places to recruit testers, especially for consumer-facing AI products.

    Start by identifying where your likely users hang out. Are you building a generative AI tool for writers? Check out writing communities on Reddit, such as r/writing or r/selfpublish, and Facebook groups for authors. Creating a new AI API for developers? You might visit programming forums like Stack Overflow or subreddits like r/programming or r/machinelearning. There are even dedicated Reddit communities like r/betatests and r/AlphaandBetausers specifically for connecting product owners with volunteer beta testers.

    When approaching communities, engage authentically. Don’t just spam “please test my app”, instead go to the chosen subreddits and provide truly helpful, detailed comments and then drop the link to your beta signup page.

    This approach of offering value first can build goodwill and attract testers who are genuinely interested in your AI. On X and LinkedIn, you can similarly share interesting content about your AI project and include a call for beta participants. Using hashtags like #betaTesting, #AI or niche tags related to your product can improve visibility. For instance, tweeting “Looking for early adopters to try our new AI interior design assistant #betatesting #homedecor #interiordesign”.

    Beyond broad social media, consider special interest communities and forums. If your AI product is domain-specific, go where the domain experts are. For a medical AI, you might reach out on medical professional forums or LinkedIn groups for doctors. For a gaming AI (say an NPC dialogue generator), gaming forums or Discord servers could be fertile ground. The key is to clearly explain what your AI does, what kind of feedback or usage you need, and what testers get in return (early access, or even small incentives). Many people love being on the cutting edge of tech and will volunteer for the novelty alone, especially if you make them feel like partners in shaping the AI.

    One caveat: recruiting from open communities can net a lot of enthusiasts, but not all will match your eventual user base. If you notice an imbalance, for example all your volunteer chatbot testers are tech-savvy 20-somethings but your target market is retirees, you may need to adjust course and recruit through other channels to fill the gaps. Social recruiting is best combined with targeted methods to ensure diversity and representativeness.

    3. Using Targeted Advertising to Attract Niche Testers

    If organic outreach isn’t yielding the specific types of testers you need, paid advertising can be an effective recruitment strategy. Targeted ads let you cast a net for exactly the demographic or interest group you want, which is extremely useful for finding niche experts or users for AI feedback.

    For example, imagine you’re fine-tuning an AI legal advisor and you really need feedback from licensed attorneys. You could run a LinkedIn ad campaign targeted at users with job titles like “Attorney” or interests in “Legal Tech.” Likewise, Facebook ads allow targeting by interests, age, location, etc., which could help find, say, small business owners to test an AI bookkeeping assistant, or teachers to try an AI education tool. As one guide suggests, “a well-targeted ad campaign on an appropriate social network could pull in some members of your ideal audience to participate”, even if they’ve never heard of your product before.

    Yes, advertising costs money, but it can be worth the investment to get high-quality feedback. For relatively little spend, you might quickly recruit a dozen medical specialists or a hundred finance professionals, groups that might be hard to find just by posting on general forums. Platforms like Facebook, LinkedIn, Twitter, and Reddit all offer ad tools that can zero in on particular communities or professions.

    When crafting your ad or sponsored post for recruitment, keep it brief and enticing. Highlight the unique opportunity (e.g. “Help shape a new AI tool for doctors: looking for MDs to give feedback on a medical chatbot, early access + Amazon gift card for participants”). Make the signup process easy (link to a simple form or landing page). And be upfront about what you’re asking for (time commitment, what testers will need to do with the AI, etc.) and what they get (incentives, early use, or just the satisfaction of contributing to innovation).

    Paid ads shine when you need specific humans at scale, on a timeline. Just be sure to monitor the sign-ups to ensure they truly fit your criteria. You may need a screener question or follow-up to verify respondents (for example, confirm someone is truly a nurse before relying on their test feedback for your health AI).

    4. Leveraging Platforms Built for Participant Recruitment

    In the last decade, a number of participant recruitment platforms have emerged to make finding the right testers or annotators much easier. These services maintain large panels of people, often hundreds of thousands, and provide tools to filter and invite those who meet your needs. For teams building generative AI products, these platforms can dramatically accelerate and improve the process of getting quality human feedback.

    Below, we discuss a few key platforms and how they fit into AI user feedback:

    • BetaTesting: is a platform expressly designed to connect product teams with real-world testers. It boasts the largest pool of real world beta testers, including everyday consumers as well as professionals and dedicated QA testers, all with 100+ targeting criteria to choose from.

      In practical terms, BetaTesting lets you specify exactly who you want, e.g. “finance professionals in North America using iPhone,” or “Android users ages 18-24 who are heavy social media users”, and then recruits those people from its community of over 450,000+ testers to try your product. For AI products, this is incredibly valuable. You can find testers who match niche demographics or usage patterns that align with your AI’s purpose, ensuring the feedback you get is relevant.

      Through BetaTesting’s platform, you can deploy test instructions, surveys, and tasks (like “try these 5 prompts with our chatbot and rate the responses”), and testers’ responses are collected in one place. This all-in-one approach takes the logistical headache out of running a beta, letting you focus on analyzing the AI feedback. BetaTesting emphasizes high-quality, vetted participants (all are ID-verified, not anonymous), which leads to more reliable feedback. Notably, BetaTesting has specific solutions for AI products, including AI product research, RLHF, evals, fine-tuning, and data collection).

      In summary, if you want a turnkey solution to find and manage great testers for a generative AI, BetaTesting is a top choice. It offers a large, diverse tester pool, fine-grained targeting, and a robust platform to gather feedback. (It’s no surprise we highlight BetaTesting here: its ability to deliver the exact audience you need makes it a preferred platform for AI user feedback.)
    • Pareto.AI: is a newer entrant that specializes in providing expert human data for AI and LLM (Large Language Model) training. Think of Pareto as a bridge between AI developers and subject-matter experts who can label data or evaluate outputs.

      This platform is particularly useful when fine-tuning an AI requires domain-specific knowledge. For example, if you need certified accountants to label financial documents for an AI, or experienced marketers to rank AI-generated ad copy. Pareto verifies the credentials of its experts and ensures they meet the skill criteria (their workforce is dubbed the top 0.01% of data labelers).

      In an AI feedback context, Pareto can be used to recruit professionals to fine-tune reward models or evaluate model outputs in areas where generic crowd feedback wouldn’t cut it. For instance, a law-focused LLM could be improved by having Pareto’s network of lawyers score the accuracy and helpfulness of its answers, feeding those judgments back into training. The advantage here is quality and credibility. You’re not just getting any crowd feedback, but expert feedback. The trade-off is that it’s a premium service (and likely costs more per participant than general crowdsourcing). For critical AI applications where mistakes are costly, this investment can be very worthwhile.
    • Prolific: is an online research platform widely used in academic and industry studies, known for its high-quality, diverse participant pool and transparent platform. Prolific makes it easy to run surveys or experiments and is increasingly used for AI data collection and model evaluation tasks, connecting researchers to a global pool of 200,000+ vetted participants for fast, reliable data.

      For AI user feedback, Prolific shines when you need a large sample of everyday end-users to test an AI feature or provide labeled feedback. For example, you could deploy a study where hundreds of people chat with your AI assistant and then answer survey questions about the experience (e.g. did the AI answer correctly? was it polite? would you use it again?). Prolific’s prescreening tools let you target users by demographics and even by specialized traits via screening questionnaires.

      One of Prolific’s strengths is data quality. Studies have found Prolific participants to be attentive and honest compared to some other online pools. If you need rapid feedback at scale, Prolific can often deliver complete results quickly, which is great for iterative tuning. Prolific is also useful for AI bias and fairness testing: you can intentionally recruit diverse groups (by age, gender, background) to see how different people perceive your AI or where it might fail.

      While Prolific participants are typically not “expert professionals” like Pareto’s, they represent a broad swath of real-world users, which is invaluable for consumer AI products.
    • Amazon Mechanical Turk (MTurk): is one of the oldest and best-known crowdsourcing marketplaces. It provides access to a massive on-demand workforce (500,000+ workers globally) for performing “Human Intelligence Tasks”, everything from labeling images to taking surveys.

      Amazon describes MTurk as “a crowdsourcing marketplace that makes it easier for individuals and businesses to outsource their processes and jobs to a distributed workforce… [it] enables companies to harness the collective intelligence, skills, and insights from a global workforce”. In the context of AI, MTurk has been used heavily to gather training data and annotations, for example, creating image captions, transcribing audio, or moderating content that trains AI models. It’s also been used for RLHF-style feedback at scale (though often without strict vetting of workers’ expertise).

      The benefit of MTurk is scale and speed at low cost. If you design a straightforward task, you can get thousands of annotations or model-rating judgments in hours. For instance, you might ask MTurk workers to rank which of two chatbot responses is better, to generate a large preference dataset. However, the quality of MTurk work can be variable. Workers come from all walks of life with varying attention levels; you have to implement quality controls (like test questions or worker qualification filters) to ensure reliable results.

      MTurk is best suited when your feedback tasks can be broken into many micro-tasks that don’t require deep expertise, e.g. collect 10,000 ratings of AI-generated sentences for fluency. It’s less ideal if you need lengthy, thoughtful responses or expert judgment, though you can sometimes screen for workers with specific backgrounds using qualifications. Many AI teams integrate MTurk with tools like Amazon SageMaker Ground Truth to manage data labeling pipelines.

      As an example of its use, the Allen Institute for AI noted they use MTurk to “build datasets that help our models learn common sense knowledge… [MTurk] provides a flexible platform that enables us to harness human knowledge to advance machine learning research.” 

      In summary, MTurk is a powerhouse for large-scale human feedback but requires careful setup to target the right workers and maintain quality.

    Each of these platforms has its niche, and they aren’t mutually exclusive. In fact, savvy AI product teams often use a combination of methods: perhaps engaging a small expert group via Pareto or internal recruitment for fine-tuning, a beta test via BetaTesting for functional product feedback for AI products, and a large-scale MTurk job for specific data labeling.

    The good news is that you don’t have to reinvent the wheel to find testers, solutions like BetaTesting and others have already assembled the crowds and experts you need, so you can focus on what feedback to ask for.

    Check it out: We have a full article on Recruiting Humans for RLHF (Reinforcement Learning from Human Feedback)

    Conclusion

    In the development of generative AI products, humans truly are the secret ingredient that turns a good model into a great product. But not just any humans will do, you need feedback from the right audience, whether that means domain experts to ensure accuracy or representative end-users to ensure usability and satisfaction.

    As we’ve discussed, many groundbreaking AI systems initially struggled until human feedback from targeted groups helped align them with real-world needs. By carefully recruiting who tests and trains your AI, you steer its evolution in the direction that best serves your customers.

    Fortunately, we have more tools than ever in 2025 to recruit and manage these ideal testers. From internal beta programs and social media outreach to dedicated platforms like BetaTesting (with its vast, high-quality tester community) and specialist networks like Pareto.AI, you can get virtually any type of tester or annotator you require.

    The key is to plan a recruitment strategy that matches your AI’s goals: use employees and loyal users for quick early feedback, reach out in communities where your target users spend time, run targeted ads or posts when you need to fill specific gaps, and leverage recruitment platforms to scale up and formalize the process.

    By investing the effort to find the right people for AI feedback, you invest in the success of your AI. You’ll catch issues that only a true user would notice, get ideas that only an expert would suggest, and ultimately build a more robust, trustworthy system. Whether you’re fine-tuning an LLM’s answers or beta testing a new AI-powered app, the insights from well-chosen humans are irreplaceable. They are how we ensure our intelligent machines truly serve and delight the humans they’re built for.

    So don’t leave your AI’s growth to chance, recruit the audiences that will push it to be smarter, safer, and more impactful. With the right humans in the loop, there’s no limit to how far your AI product can go.

    Have questions? Book a call in our call calendar.

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  • Top 5 Beta Testing Companies Online

    Beta testing is a critical practice for product and engineering teams to test and get feedback for their apps, websites, and physical products with real users before a new product launch or feature launch. By catching bugs, gathering UX feedback, and ensuring performance in real-world scenarios, beta testing helps teams launch with confidence. Fortunately, there are several specialized companies that make beta testing easier by providing platforms, communities of testers, and advanced tools.

    This article explores five top online beta testing companies: 

    BetaTesting, Applause, Centercode, Rainforest QA, and UserTesting, discussing their strengths, specializations, how they differ, any AI capabilities they offer, and examples of their success. Each of these services has something unique to offer for startups and product teams looking to improve product quality and user satisfaction.

    BetaTesting

    BetaTesting.com is one of the top beta testing companies, and provides a web platform to connect companies with a large community of real-world beta testers. BetaTesting has grown into a robust solution for crowdsourced beta testing and user research and is one of the top rated companies by independent review provider G2 for crowdtesting services.

    The platform boasts a network of over 450,000 participants across diverse demographics, allowing teams to recruit testers that match their target audience. BetaTesting’s mission is to simplify the process of collecting and analyzing user feedback, making even complex data easy to understand in practical terms. This makes it especially appealing to startups that need actionable insights without heavy lifting.

    Key strengths and features of BetaTesting include:

    Recruiting High Quality Real People: BetaTesting maintains their own first-party panel of verified, vetted, non-anonymous real-world people. They make it easy to filter and select testers based on 100’s of targeting criteria ranging from demographics like age, location, education, to advanced targeting such as product usage, health and wellness, and work life and tools.

    BetaTesting provides participant rewards that are 10X higher than many competitive testing and research platforms. This is helpful because your target audience probably isn’t struggling to make $5 an hour by clicking test links all day like those on many other research platforms. Providing meaningful incentives allows BetaTesting to recruit high quality people that match your target audience. These are real consumers and business professionals spanning every demographic, interest, and profession – not professional survey takers or full-time taskers. The result is higher quality data and feedback.

    Anti-Fraud Procedures: BetaTesting is a leader in providing a secure and fraud-free platform and incorporating features and tools to ensure you’re getting quality feedback from real people. Some of these steps include:

    • ID verification for testers
    • No VPN or anonymous IPs. Always know your testers are located where they say they are.
    • SMS verification
    • LinkedIn integration
    • Validation of 1 account per person
    • Anti-bot checks and detection for AI use
    • Fraud checks through the incentive partner Tremendous

    Flexible Testing Options in the Real World: BetaTesting supports anything from one-time “bug hunt” sessions to multi-week beta trials. Teams can run short tests or extended programs spanning days or months, adapting to their needs. This flexibility is valuable for companies that iterate quickly or plan to conduct long-term user research.

    Testers provide authentic feedback on real devices in natural environments. The platform delivers detailed bug reports and even usability video recordings of testers using the product. This helps uncover issues with battery usage, performance, and user experience under real conditions, not just lab settings.

    BetaTesting helps collect feedback in three core ways:

    • Surveys (written feedback)
    • Videos (usability videos, unboxing videos, etc)
    • Bug reports

    Check it out: Test types you can run on BetaTesting

    Human Feedback for AI Products: When building AI products and improving AI models, it’s critical to get feedback and data from your users and customers. BetaTesting helps companies get human feedback for AI to build better/smarter models, agents & AI product experiences. This includes targeting the right people to power AI product research, evals, fine-tuning, and data collection.

    BetaTesting’s focus on real-world testing at scale has led to tangible success stories. For example, Triinu Magi (CTO of Neura) noted how quick and adaptive the process was: 

    “The process was very easy and convenient. BetaTesting can move very fast and adapt to our changing needs. It helped us understand better how the product works in the real world. We improved our battery consumption and also our monitoring capabilities.”

    Another founder, Robert Muño, co-founder of Typeform, summed up the quality of BetaTesting testers: 

    “BetaTesting testers are smart, creative and eager to discover new products. They will get to the essence of your tool in no time and give you quality feedback enough to shape your roadmap for well into the future.”

    These testimonials underscore BetaTesting’s strength in rapidly providing companies with high-quality testers and actionable feedback. While BetaTesting also incorporates AI features throughout the platform, including AI analytics to help interpret tester feedback, including summarization, transcription, sentiment analysis, and more.

    Overall, BetaTesting excels in scalable beta programs with real people in real environments and is a perfect fit for product teams that want to get high quality testing and feedback from real people, not professional survey clickers or taskers.

    Applause

    Applause grew out of one of the first crowdtesting sites called uTest and markets itself as a leading provider of digital quality assurance. Founded in 2007 as uTest, Applause provides fully managed testing services by leveraging a big community of professional testers. Applause indicates that they have over 1.5 million digital testers. This expansive reach means Applause can test digital products in practically every real-world scenario, across all devices, OSes, browsers, languages, and locations.

    For a startup or enterprise releasing a new app, Applause’s community can surface issues that might only appear in specific regions or on obscure device configurations, providing confidence that the product works for “everyone, everywhere.”

    What sets Applause apart is its comprehensive, managed approach to quality through fully managed testing services:

    Full-Service Testing – Applause assigns a project manager and a hand-picked team of testers for each client engagement. They handle the test planning, execution, and results delivery, so your internal team isn’t burdened with logistics. The testers can perform exploratory testing to find unexpected bugs and also execute structured test cases to verify specific functionality. This dual approach ensures both creative real-world issues and core requirements are covered. Because it’s fully managed, it can be a lot more expensive than self-service alternatives.

    Diverse Real-World Coverage – With testers in over 200 countries and on countless device/browser combinations, Applause can cover a wide matrix of testing conditions.  For product teams aiming at a global audience, this diversity is invaluable.

    Specialty Testing Domains – Applause’s services span beyond basic functional testing. They offer usability and user experience (UX) studies, payment workflow testing, accessibility audits, AI model training/validation, voice interface testing, security testing, and more. For example, Applause has been trusted to expand accessibility testing for Cisco’s Webex platform, ensuring the product works for users with disabilities.

    AI-Powered Platform – Applause has started to integrate artificial intelligence into its processes like some of the other companies on this list. The company incorporated AI-driven capabilities, built with IBM watsonx, into its own testing platform to help improve speed, accuracy and scale” of test case management. Additionally, Applause launched offerings for testing generative AI systems, including providing human “red teaming” to probe generative AI models for security vulnerabilities.

    In short, Applause uses AI both as a tool to streamline testing and as a domain, giving clients feedback on AI-driven products.

    Applause’s track record includes many success stories, especially for enterprise product teams.

    As an example of Applause’s impact, IBM noted that Applause enables brands to test digital experiences globally to retain customers, citing Applause’s ability to ensure quality across all devices and demographics.

    If you’re a startup or a product team seeking fully managed quality assurance through crowdtesting, Applause is a good choice. It combines the power of human insight with professional management, a formula that has helped make crowdtesting an industry standard.

    Centercode

    Centercode takes a slightly different angle on beta testing: it provides a robust platform for managing beta programs and user testing with an emphasis on automation and data handling. Centercode has been a stalwart in the beta testing space for over 20 years, helping tech companies like Google, HP, and Verizon run successful customer testing programs. Instead of primarily supplying external testers, it excels at giving product teams the tools to organize their own beta tests, whether with employees, existing customers, or smaller user groups.

    Think of Centercode as the “internal infrastructure” companies can use to orchestrate beta feedback, offering a software platform to facilitate the process of recruiting testers, distributing builds, collecting feedback, and analyzing bug reports in one centralized hub.

    Centercode’s key strengths for startups and product teams include:

    Automation and Efficiency: Centercode aims to build automation into each phase of beta testing to eliminate tedious tasks. For instance, an AI assistant called “Ted AI” can “generate test plans, surveys, and reports in seconds”, send personalized reminders to testers, and accelerate feedback cycles. This can help lean product teams manage the testing process as it reduces the manual effort needed to run a thorough beta test.

    Centralized Feedback & Issue Tracking: All tester feedback (bug reports, suggestions, survey responses) flows into one platform. Testers can log issues directly in Centercode, which makes them immediately visible to all stakeholders. No more juggling spreadsheets or emails. Bugs and suggestions are tracked, de-duplicated, and scored intelligently to highlight what matters most.

    Rich Media and Integrations: Recognizing the need for deeper insight, Centercode now enables video feedback through a feature called Replays, which can records video sessions and provide analysis on top. Seeing a tester’s experience on video can reveal usability issues that a written bug report might miss. Similar to BetaTesting, it integrates with developer tools and even app stores, for example, it connects with Apple TestFlight and Google Play Console to automate mobile beta distribution and onboarding of testers. This saves time for product teams managing mobile app betas.

    Expert Support and Community Management: Centercode offers managed services to help run the program if a team is short on resources. Companies can hire Centercode to provide program management experts who handle recruiting testers, setting up test projects, and keeping participants engaged. This on-demand support is useful for companies that are new to beta testing best practices. Furthermore, Centercode enables companies to nurture their own tester communities over time.

    Crucially, Centercode has also embraced AI to supercharge beta testing. The platform’s new AI capabilities were highlighted in its 2025 launch: 

    “Centercode 10x builds on two decades of beta testing leadership, introducing AI-driven automation, real-world video insights, seamless app store integrations, and expert support to help teams deliver better products, faster.” 

    By integrating AI, Centercode marries efficiency with depth, for instance, automatically scoring bug reports by likely impact.

    Centercode’s approach is ideal for product managers who want full control and visibility into the testing process. A successful use case can be seen with companies that have niche user communities or hardware products: they use Centercode to recruit the right enthusiasts, gather their feedback in a structured way, and turn that into actionable insights for engineering. Because Centercode is an all-in-one platform, it ensures nothing falls through the cracks. 

    For any startup or product team that wants to run a high-quality beta program (whether with 20 testers or 2,000 testers), Centercode provides the scalable, automated backbone to do so effectively.

    Check this article out: AI User Feedback: Improving AI Products with Human Feedback

    Rainforest QA

    Rainforest QA is primarily an automated QA company that focuses on automated functional testing, designed for the rapid pace of SaaS startups and agile development teams. Rainforest is best known for its testing platform that blends automated and manual powered testing on defined QA test scripts. Unlike traditional beta platforms that test in the real-world on real-devices, Rainforest is powered a pool of inexpensive overseas testers (available 24/7) who execute tests in a controlled, cloud-based environment using virtual machines and emulated devices.

    Rainforest’s philosophy is to integrate testing seamlessly into development cycles, often enabling companies to run tests for each code release and get results back in minutes. This focus on speed and integration makes Rainforest especially appealing to product teams practicing continuous delivery.

    Standout features and strengths include:

    Fast Test Results for Defined QA Test Scripts: Rainforest is engineered for quick turnaround. When you write test scenarios and submit them, their crowd of QA specialists executes them in parallel. As a result, test results often come back in an average of 17 minutes after submission, an astonishing speed. Testers are available around the clock, so even a last-minute build on a Friday night can be tested immediately. This speed instills confidence for fast-moving startups to push updates without lengthy QA delays.

    Consistent, Controlled Environments: A unique differentiator of Rainforest is that all tests run on virtual machines (VMs) in the cloud, ensuring each test runs in a clean, identical environment. Testers use these VMs rather than their own unpredictable devices. This approach avoids the “works on my machine” syndrome, results are reliable and reproducible because every tester sees the same environment.

    While Applause or BetaTesting focus on real-world device variation, Rainforest’s model trades some of that for consistency; it’s like a lab test versus an in-the-wild test. This could mean fewer false alarms due to unique device settings, and easier bug replication by developers, but also a difficulty in finding edge cases and testing your product in real-world conditions.

    No-Code Test Authoring with AI Assistance: Rainforest enables non-engineers (like product managers or designers) to create automated test cases in plain English using a no-code editor. Recently, they’ve supercharged this capability with generative AI. The platform can generate test scripts quickly from plain-English prompts  essentially, you describe a user scenario and the AI helps build the test steps. Moreover, Rainforest employs AI self-healing: if minor changes in your app’s UI would normally break a test, the AI can automatically adjust selectors or steps so the test doesn’t fail on a trivial change. This dramatically reduces test maintenance, a common pain in automation. By integrating AI into test creation and maintenance, Rainforest ensures that even as your product UI evolves, your test suite keeps up with minimal manual updates.

    Integrated Manual and Automated Testing: Rainforest offers both fully automated tests (run by robots) and crowd-powered manual tests, all through one platform. For example, you can run a suite of regression tests automated by the Rainforest system, and also trigger an exploratory test where human testers try to break the app without a script. All results – with screenshots, videos, logs – come back into a unified dashboard.

    Every test run is recorded on video with detailed logs, so developers get rich diagnostics for any failures. Rainforest even sends multiple testers to execute the same test in parallel and cross-verifies their results for accuracy, ensuring you don’t get false positives.

    Rainforest QA has proven valuable for many startups who need a scalable QA process without building a large in-house QA team. One of its benefits is the ability to integrate into CI/CD pipelines – for instance, running a suite of tests on each GitHub pull request or each deployment automatically. This catches bugs early and speeds up release cycles.

    All told, Rainforest QA is a great choice for startups and companies that need script-based QA functional testing and prioritize speed, continuous integration, and reliable test automation. It’s like having a QA team on-call for quick testing to cut out repetitive grunt work.

    UserTesting

    UserTesting is a bit different than the other platforms on this list because they focus primarily on usability videos. While most of the pure beta testing platforms include the ability to report bugs, validate features, and get high-level user experience feedback, UserTesting is primarily about using usability videos ( screen recordings + audio) to understanding why users might struggle with your product or how they feel about it.

    The UserTesting platform provides on-demand access to a panel of participants who match your target audience, and it records video sessions of these users as they perform tasks on your app, website, or prototype. You get to watch and hear real people using your product, voicing their thoughts and frustrations, which is incredibly insightful for product managers and UX designers. For startups, this kind of feedback can be pivotal in refining the user interface or onboarding flow before a broader launch.

    UserTesting has since expanded through the merger with UserZoom to include many of the quick UX design-focused tests that UserZoom was previously known for. This include things like card sorting, tree testing, click testing, etc.

    The core strengths and differentiators of UserTesting are:

    Specialization in Usability Videos: UserTesting specializes in usability videos. This means that the platform is primarily about gathering human insights through video: what users like, what confuses them, what they expect. The result is typically a richer understanding of your product’s usability. For example, you might discover through UserTesting that new users don’t notice a certain button or can’t figure out a feature, leading you to redesign it before launch.

    Live User Narratives on Video: UserTesting’s hallmark is the video think-aloud session. You define tasks or questions, and the testers record themselves as they go through them, often speaking their thoughts. You receive videos (and transcripts) showing exactly where someone got frustrated or delighted. This qualitative data (facial expressions, tone of voice, click paths, etc.) is something purely quantitative beta testing can miss. It’s like doing a live usability lab study, but online and much faster. The platform also captures on-screen interactions and can provide session recordings for later analysis.

    Targeted Audience and Test Templates: UserTesting has a broad panel of participants worldwide, and you can filter them by demographics, interests, or even by certain behaviors. This ensures the feedback is relevant to your product’s intended market. Moreover, UserTesting provides templates and guidance for common test scenarios (like onboarding flows, e-commerce checkout, etc.), which is helpful for startups new to user research.

    AI-Powered Analysis of Feedback: Dealing with many hour-long user videos could be time-consuming, so UserTesting has introduced AI capabilities to help analyze and summarize the feedback. Their AI Insight Summary (leveraging GPT technology) automatically reviews the verbal and behavioral data in session videos to identify key themes and pain points. It can produce a succinct summary of what multiple users struggled with, which saves researchers time.

    The value of UserTesting is perhaps best illustrated by real use cases. One example is ZoomShift (a SaaS company) who drastically improved its user onboarding after running tests on UserTesting. By watching users attempt to sign up and get started, the founders identified exactly where people were getting stuck. They made changes and saw setup conversion rates jump from 12% to 87% – a >700% improvement in conversions. As the co-founder reported, 

    “We used UserTesting to get the feedback we needed to increase our setup conversions from 12% to 87%. That’s a jump of 75 percentage points!”

    Many product teams find that a few hours of watching user videos can reveal UI and UX problems that, once fixed, significantly boost engagement or sales.

    UserTesting is widely used not only by startups but also by design and product teams at large companies (Adobe, Canva, and many others are referenced as customers). It’s an essential tool for human-centered design, ensuring that products are intuitive and enjoyable.

    In summary, if your team’s goal is to understand your users deeply and create an optimal user interface flows, UserTesting is the go-to platform. It complements the more high-level user experience and bug-oriented testing services by provided by the core beta testing providers and provdes the voice of the customer directly, helping you build products that truly resonate with your target audience.

    Now that you know the Top 5 Beta Testing companies online, check out: Top 10 AI Terms Startups Need to Know

    Still Thinking About Which One To Choose?

    Get in touch with our team at BetaTesting to discuss your needs. Of course we’re biased, but we’re happy to tell you if we feel another company would be a better fit for your needs.

    Have questions? Book a call in our call calendar.

    Book a Call or Demo
    Get a Quote Today

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  • Recruiting Humans for RLHF (Reinforcement Learning from Human Feedback)

    Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal technique for aligning AI systems, especially generative AI models like large language models (LLMs) with human expectations and values. By incorporating human preferences into the training loop, RLHF helps AI produce outputs that are more helpful, safe, and contextually appropriate.

    This article provides a deep dive into RLHF: what it is, its benefits and limitations, when and how it fits into an AI product’s development, the tools used to implement it, and strategies for recruiting human participants to provide the critical feedback that drives RLHF. In particular, we will highlight why effective human recruitment (and platforms like BetaTesting) is crucial for RLHF success.

    Here’s what we will explore:

    1. What is RLHF?
    2. Benefits of RLHF
    3. Limitations of RLHF
    4. When Does RLHF Occur in the AI Development Timeline?
    5. Tools Used for RLHF
    6. How to Recruit Humans for RLHF

    What is RLHF?

    Reinforcement learning from human feedback (RLHF) is a machine learning technique in which a “reward model” is trained with direct human feedback, then used to optimize the performance of an artificial intelligence agent through reinforcement learning – IBM

    In essence, humans guide the AI by indicating which outputs are preferable, and the AI learns to produce more of those preferred outputs. This method is especially useful for tasks where the notion of “correct” output is complex or subjective.

    For example, it would be impractical (or even impossible) for an algorithmic solution to define ‘funny’ in mathematical terms – but easy for humans to rate jokes generated by a large language model (LLM). That human feedback, distilled into a reward function, could then be used to improve the LLM’s joke writing abilities. In such cases, RLHF allows us to capture human notions of quality (like humor, helpfulness, or style) which are hard to encode in explicit rules.

    Originally demonstrated on control tasks (like training agents to play games), RLHF gained prominence in the realm of LLMs through OpenAI’s research. Notably, the InstructGPT model was fine-tuned with human feedback to better follow user instructions, outperforming its predecessor GPT-3 in both usefulness and safety.

    This technique was also key to training ChatGPT – “when developing ChatGPT, OpenAI applies RLHF to the GPT model to produce the responses users want. Otherwise, ChatGPT may not be able to answer more complex questions and adapt to human preferences the way it does today.” In summary, RLHF is a method to align AI behavior with human preferences by having people directly teach the model what we consider good or bad outputs.

    Check it out: We have a full article on AI Product Validation With Beta Testing

    Benefits of RLHF

    Incorporating human feedback into AI training brings several important benefits, especially for generative AI systems:

    • Aligns output with human expectations and values: By training on human preferences, AI models become “cognizant of what’s acceptable and ethical human behavior” and can be corrected when they produce inappropriate or undesired outputs.

      In practice, RLHF helps align models with human values and user intent. For instance, a chatbot fine-tuned with RLHF is more likely to understand what a user really wants and stick within acceptable norms, rather than giving a literal or out-of-touch answer.
    • Produces less harmful or dangerous output: RLHF is a key technique for steering AI away from toxic or unsafe responses. Human evaluators can penalize outputs that are offensive, unsafe, or factually wrong, which trains the model to avoid them.

      As a result, RLHF-trained models like InstructGPT and ChatGPT generate far fewer hateful, violent, or otherwise harmful responses compared to uninstructed models. This fosters greater trust in AI assistants by reducing undesirable outputs.
    • More engaging and context-aware interactions: Models tuned with human feedback provide responses that feel more natural, relevant, and contextually appropriate. Human raters often reward outputs that are coherent, helpful, or interesting.

      OpenAI reported that RLHF-tuned models followed instructions better, maintained factual accuracy, and avoided nonsense or “hallucinations” much more than earlier models. In practice, this means an RLHF-enhanced AI can hold more engaging conversations, remember context, and respond in ways that users find satisfying and useful.
    • Ability to perform complex tasks aligned with human understanding: RLHF can unlock a model’s capability to handle nuanced or difficult tasks by teaching it the “right” approach as judged by people. For example, humans can train an AI to summarize text in a way that captures the important points, or to write code that actually works, by giving feedback on attempts.

      This human-guided optimization enables LLMs with lesser parameters to perform better on challenging queries. OpenAI noted that its labelers preferred outputs from the 1.3B-parameter version of InstructGPT over even outputs from the 175B-parameter version of GPT-3. – showing that targeted human feedback can beat brute-force scale in certain tasks.
      Overall, RLHF allows AI to tackle complex, open-ended tasks in ways that align with what humans consider correct or high-quality.

    Limitations of RLHF

    Despite its successes, RLHF also comes with notable challenges and limitations:

    • Expensive and resource-intensive: Obtaining high-quality human preference data is costly and does not easily scale. Human preference data is expensive. The need to gather firsthand human input can create a costly bottleneck that limits the scalability of the RLHF process.

      Training even a single model can require thousands of human feedback judgments, and employing experts or large crowds of annotators can drive up costs. This is one reason companies are researching partial automation of the feedback process (for example, AI-generated feedback as a supplement) to reduce reliance on humans.
    • Subjective and inconsistent feedback: Human opinions on what constitutes a “good” output can vary widely. 

      “Human input is highly subjective. It’s difficult, if not impossible, to establish firm consensus on what constitutes ‘high-quality’ output, as human annotators will often disagree… on what ‘appropriate’ model behavior should mean.”

      In other words, there may be no single ground truth for the model to learn, and feedback can be noisy or contradictory. This subjectivity makes it hard to perfectly optimize to “human preference,” since different people prefer different things.
    • Risk of bad actors or trolling: RLHF assumes feedback is provided in good faith, but that may not always hold. Poorly incentivized crowd workers might give random or low-effort answers, and malicious users might try to teach the model undesirable behaviors.

      Researchers have even identified “troll” archetypes who give harmful or misleading feedback. Robust quality controls and careful participant recruitment are needed to mitigate this issue (more on this in the recruitment section below).
    • Bias and overfitting to annotators:  An RLHF-tuned model will reflect the perspectives and biases of those who provided the feedback. If the pool of human raters is narrow or unrepresentative, the model can become skewed. 

      For example, a model tuned only on Western annotators’ preferences might perform poorly for users from other cultures. It’s essential to use diverse and well-balanced feedback sources to avoid baking in bias.

    In summary, RLHF improves AI alignment but is not a silver bullet – it demands significant human effort, good experimental design, and continuous vigilance to ensure the feedback leads to better, not worse, outcomes.

    When Does RLHF Occur in the AI Development Timeline?

    RLHF is typically applied after a base AI model has been built, as a fine-tuning and optimization stage in the AI product development lifecycle. By the time you’re using RLHF, you usually have a pre-trained model that’s already learned from large-scale data; RLHF then adapts this model to better meet human expectations.

    The RLHF pipeline for training a large language model usually involves multiple phases:

    1. Supervised fine-tuning of a pre-trained model: Before introducing reinforcement learning, it’s common to perform supervised fine-tuning (SFT) on the model using example prompts and ideal responses.

      This step “primes” the model with the format and style of responses we want. For instance, human trainers might provide high-quality answers to a variety of prompts (Q&A, writing tasks, etc.), and the model is tuned to imitate these answers.

      SFT essentially “‘unlocks’ capabilities that GPT-3 already had, but were difficult to elicit through prompt engineering alone. In other words, it teaches the model how it should respond to users before we start reinforcement learning.
    2. Reward model training (human preference modeling): Next, we collect human feedback on the model’s outputs to train a reward model. This usually involves showing human evaluators different model responses and having them rank or score which responses are better.

      For example, given a prompt, the model might generate multiple answers; humans might prefer Answer B over Answer A, etc. These comparisons are used to train a separate neural network – the reward model – that takes an output and predicts a reward score (how favorable the output is).

      Designing this reward model is tricky because asking humans to give absolute scores is hard; using pairwise comparisons and then mathematically normalizing them into a single scalar reward has proven effective. The reward model effectively captures the learned human preferences.
    3. Policy optimization via reinforcement learning: In the final phase, the original model (often called the “policy” in RL terms) is further fine-tuned using reinforcement learning algorithms, with the reward model providing the feedback signal.

      A popular choice is Proximal Policy Optimization (PPO), which OpenAI used for InstructGPT and ChatGPT. The model generates outputs, the reward model scores them, and the model’s weights are adjusted to maximize the reward. Care is taken to keep the model from deviating too much from its pre-trained knowledge (PPO includes techniques to prevent the model from “gaming” the reward by producing gibberish that the reward model happens to score highly.

      Through many training iterations, this policy optimization step trains the model to produce answers that humans (as approximated by the reward model) would rate highly. After this step, we have a final model that hopefully aligns much better with human-desired outputs.

    It’s worth noting that pre-training (the initial training on a broad dataset) is by far the most resource-intensive part of developing an LLM. The RLHF fine-tuning stages above are relatively lightweight in comparison – for example, OpenAI reported that the RLHF process for InstructGPT used <2% of the compute that was used to pre-train GPT-3.

    RLHF is a way to get significant alignment improvements without needing to train a model from scratch or use orders of magnitude more data; it leverages a strong pre-trained foundation and refines it with targeted human knowledge.

    Check it out: Top 10 AI Terms Startups Need to Know

    Tools Used for RLHF

    Implementing RLHF for AI models requires a combination of software frameworks, data collection tools, and evaluation methods, as well as platforms to source the human feedback providers. Key categories of tools include:

    Participant recruitment platforms: A crucial “tool” for RLHF is the source of human feedback providers. You need humans (often lots of them) to supply the preferences, rankings, and demonstrations that drive the whole process. This is where recruitment platforms come in (discussed in detail in the next section).

    In brief, some options include crowdsourcing marketplaces like Amazon Mechanical Turk, specialized AI data communities, or beta testing platforms to get real end-users involved. The quality of the human feedback is paramount, so choosing the right recruitment approach (and platform) significantly impacts RLHF outcomes.

    BetaTesting is a platform with a large community of vetted, real-world testers that can be tapped for collecting AI training data and feedback at scale

    Other services like Pareto or Surge AI maintain expert labeler networks to provide high-accuracy RLHF annotations, while platforms like Prolific recruit diverse participants who are known for providing attentive and honest responses. Each has its pros and cons, which we’ll explore below.

    RLHF training frameworks and libraries: Specialized libraries help researchers train models with RLHF algorithms. For example, Hugging Face’s TRL (Transformer Reinforcement Learning) library provides “a set of tools to train transformer language models” with methods like supervised fine-tuning, reward modeling, and PPO/other optimization algorithms.

    Open-source frameworks such as DeepSpeed-Chat (by Microsoft), ColossalChat (by Colossal AI), and newer projects like OpenRLHF have emerged to facilitate RLHF at scale. These frameworks handle the complex “four-model” setup (policy, reward model, reference model, optimizer) and help with scaling to large model sizes. In practice, teams leveraging RLHF often start with an existing library rather than coding the RL loop from scratch.

    Data labeling & annotation tools: Since RLHF involves collecting a lot of human feedback data (e.g. comparisons, ratings, corrections), robust annotation tools are essential. General-purpose data labeling platforms like Label Studio and Encord now offer templates or workflows specifically for collecting human preference data for RLHF. These tools provide interfaces for showing prompts and model outputs to human annotators and recording their judgments.

    Many organizations also partner with data service providers: for instance, Appen (a data annotation company) has an RLHF service that leverages a carefully curated crowd of diverse human annotators with domain expertise to supply high-quality feedback. Likewise, Scale AI offers an RLHF platform with an intuitive interface and collaboration features to streamline the feedback process for labelers.

    Such platforms often come with built-in quality control (consistency checks, gold standard evaluations) to ensure the human data is reliable.

    Evaluation tools and benchmarks: After fine-tuning a model with RLHF, it’s critical to evaluate how much alignment and performance have improved. This is done through a mix of automated benchmarks and further human evaluation.

    A notable tool is OpenAI Evals, an open-source framework for automated evaluation of LLMs. Developers can define custom evaluation scripts or use community-contributed evals (covering things like factual accuracy, reasoning puzzles, harmlessness tests, etc.) to systematically compare their RLHF-trained model against baseline models. Besides automated tests, one might run side-by-side user studies: present users with responses from the new model vs. the old model or a competitor, and ask which they prefer.

    OpenAI’s launch of GPT-4, for example, reported that RLHF doubled the model’s accuracy on challenging “adversarial” questions – a result discovered through extensive evaluation. Teams also monitor whether the model avoids the undesirable outputs it was trained against (for instance, testing with provocative prompts to see if the model stays polite and safe).

    In summary, evaluation tools for RLHF range from code-based benchmarking suites to conducting controlled beta tests with real people in order to validate that the human feedback truly made the model better.

    How to Recruit Humans for RLHF

    Obtaining the “human” in the loop for RLHF can be challenging – the task requires people who are thoughtful, diligent, and ideally somewhat knowledgeable about the context.

    As one industry source notes, 

    “unlike typical data-labeling tasks, RLHF demands in-depth and honest feedback. The people giving that feedback need to be engaged, invested, and ready to put the time and effort into their answers.”

    This means recruiting the right participants is crucial. Here are some common strategies for recruiting humans for RLHF projects, and how they stack up:

    Internal recruitment (employees or existing users):  One way to get reliable feedback is to recruit from within your organization or current user base. For example, a product team might have employees spend time testing a chatbot and providing feedback, or invite power-users of the product to give input.

    The advantage is that these people often have domain expertise and a strong incentive to improve the AI. They might also understand the company’s values well (helpful for alignment). However, internal pools are limited in size and can introduce bias – employees might think alike, and loyal customers might not represent the broader population.

    This approach works best in early stages or for niche tasks where only a subject-matter expert can evaluate the model. It’s essentially a “friends-and-family” beta test for your AI.

    Social media, forums, and online communities:  If you have an enthusiastic community or can tap into AI discussion forums, you may recruit volunteers. Announcing an “AI improvement program” on Reddit, Discord, or Twitter, for instance, can attract people interested in shaping AI behavior.

    A notable example is the OpenAssistant project, which crowd-sourced AI assistant conversations from over 13,500 volunteers worldwide. These volunteers helped create a public dataset for RLHF, driven by interest in an open-source ChatGPT alternative. Community-driven recruitment can yield passionate contributors, but keep in mind the resulting group may skew towards tech-savvy or specific demographics (not fully representative).

    Also, volunteers need motivation – many will do it for altruism or curiosity, but retention can be an issue without some reward or recognition. This approach can be excellent for open projects or research initiatives where budget is limited but community interest is high.

    Paid advertising and outreach: Another route is to recruit strangers via targeted ads or outreach campaigns. For instance, if you need doctors to provide feedback for a medical AI, you might run LinkedIn or Facebook ads inviting healthcare professionals to participate in a paid study. Or more generally, ads can be used to direct people to sign-up pages to become AI model “testers.”

    This method gives you control over participant criteria (through ad targeting) and can reach people outside existing platforms. However, it requires marketing effort and budget, and conversion rates can be low (not everyone who clicks an ad will follow through to do tedious feedback tasks). It’s often easier to leverage existing panels and platforms unless you need a very specific type of user that’s hard to find otherwise.

    If using this approach, clarity in the ad (what the task is, why it matters, and that it’s paid or incentivized) will improve the quality of recruits by setting proper expectations.

    Participant recruitment platforms:  In many cases, the most efficient solution is to use a platform specifically designed to find and manage participants for research or testing. Several such platforms are popular for RLHF and AI data collection:

    • BetaTesting: is a user research and beta-testing platform with a large pool of over 450,000 vetted participants across various demographics, devices, and locations.

      We specialize in helping companies collect feedback, bug reports, and “human-powered data for AI” from real-world users. The platform allows targeting by 100+ criteria (age, gender, tech expertise, etc.) and supports multi-day or iterative test campaigns.

      For RLHF projects, BetaTesting can recruit a cohort of testers who interact with your AI (e.g., try prompts and rate responses) in a structured way. Because the participants are pre-vetted and the process is managed, you often get higher-quality feedback than a general crowd marketplace. BetaTesting’s focus on real user experience means participants tend to give more contextual and qualitative feedback, which can enrich RLHF training (for instance, explaining why a response was bad, not just rating it).

      In practice, BetaTesting is an excellent choice when you want high-quality, diverse feedback at scale without having to build your own community from scratch – the platform provides the people and the infrastructure to gather their input efficiently.
    • Pareto (AI): is a service that offers expert data annotators on demand for AI projects, positioning itself as a premium solution for RLHF and other data needs. Their approach is more hands-on – they assemble a team of trained evaluators for your project and manage the process closely.

      Pareto emphasizes speed and quality, boasting “expert-vetted data labelers” and “industry-leading accuracy” in fine-tuning LLMs. Clients define the project and Pareto’s team executes it, including developing guidelines and conducting rigorous quality assurance. This is akin to outsourcing the human feedback loop to professionals.

      It can be a great option if you have the budget and need very high-quality, domain-specific feedback (for example, fine-tuning a model in finance or law with specialists, ensuring consistent and knowledgeable ratings). The trade-off is cost and possibly less transparency or control compared to running a crowdsourced approach. For many startups or labs, Pareto might be used on critical alignment tasks where errors are costly.
    • Prolific: is an online research participant platform initially popular in academic research, now also used for AI data collection. Prolific maintains a pool of 200,000+ active participants who are pre-screened and vetted for quality and ethics. Researchers can easily set up studies and surveys, and Prolific handles recruiting participants that meet the study’s criteria.

      For RLHF, Prolific has highlighted its capability to provide “a diverse pool of participants who give high-quality feedback on AI models” – the platform even advertises use cases like tuning AI with human feedback. The key strengths of Prolific are data quality and participant diversity. Studies (and Prolific’s own messaging) note that Prolific respondents tend to pay more attention and give more honest, detailed answers than some other crowdsourcing pools.

      The platform also makes it easy to integrate with external tasks: you can, for example, host an interface where users chat with your model and rate it, and simply give Prolific participants the link. If your RLHF task requires thoughtful responses (e.g., writing a few sentences explaining preferences) and you want reliable people, Prolific is a strong choice.

      The costs are higher per participant than Mechanical Turk, but you often get what you pay for in terms of quality. Prolific also ensures participants are treated and paid fairly, which is ethically important for long-term projects.
    • Amazon Mechanical Turk (MTurk): is one of the oldest and largest crowd-work platforms, offering access to a vast workforce to perform micro-tasks for modest pay. Many early AI projects (and some current ones) have used MTurk to gather training data and feedback.

      On the plus side, MTurk can deliver fast results at scale – if you post a simple RLHF task (like “choose which of two responses is better” with clear instructions), you could get thousands of judgments within hours, given the size of the user base. It’s also relatively inexpensive per annotation. However, the quality control burden is higher: MTurk workers vary from excellent to careless, and without careful screening and validation you may get noisy data. For nuanced RLHF tasks that require reading long texts or understanding context, some MTurk workers may rush through just to earn quick money, which is problematic.

      Best practices include inserting test questions (to catch random answers), requiring a qualification test, and paying sufficiently to encourage careful work. Scalability can also hit limits if your task is very complex – fewer Turkers might opt in.

      It’s a powerful option for certain types of feedback (especially straightforward comparisons or binary acceptability votes) and has been used in notable RLHF implementations. But when ultimate quality and depth of feedback are paramount, many teams now prefer curated platforms like those above. MTurk remains a useful tool in the arsenal, particularly if used with proper safeguards and for well-defined labeling tasks.

    Each recruitment method can be effective, and in fact many organizations use a combination. For example, you might start with internal experts to craft an initial reward model, then use a platform like BetaTesting to get a broader set of evaluators for scaling up, and finally run a public-facing beta with actual end-users to validate the aligned model in the wild. The key is to ensure that your human feedback providers are reliable, diverse, and engaged, because the quality of the AI’s alignment is only as good as the data it learns from.

    No matter which recruitment strategy you choose, invest in training your participants and maintaining quality. Provide clear guidelines and examples of good vs. bad outputs. Consider starting with a pilot: have a small group do the RLHF task, review their feedback, and refine instructions before scaling up. Continuously monitor the feedback coming in – if some participants are giving random ratings, you may need to replace them or adjust incentives.

    Remember that RLHF is an iterative, ongoing process (“reinforcement” learning is never really one-and-done). Having a reliable pool of humans to draw from – for initial training and for later model updates – can become a competitive advantage in developing aligned AI products.

    Check it out: We have a full article on AI in User Research & Testing in 2025: The State of The Industry

    Conclusion

    RLHF is a powerful approach for making AI systems more aligned with human needs, but it depends critically on human collaboration. By understanding where RLHF fits into model development and leveraging the right tools and recruitment strategies, product teams and researchers can ensure their AI not only works, but works in a way people actually want.

    With platforms like BetaTesting and others making it easier to harness human insights, even smaller teams can implement RLHF to train AI models that are safer, more useful, and more engaging for their users.

    As AI continues to evolve, keeping humans in the loop through techniques like RLHF will be vital for building technology that genuinely serves and delights its human audience.

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  • AI Human Feedback: Improving AI Products with Human Feedback

    Building successful AI-powered products isn’t just about clever algorithms – it’s also about engaging real users at every step. Human feedback acts as a guiding compass for AI models, ensuring they learn the right lessons and behave usefully.

    In this article, we’ll explore when to collect human feedback in the AI development process, the types of feedback that matter, and how to gather and use that feedback effectively. This article is geared to product managers, user researchers, engineers, and entrepreneurs who can turn these ideas into action.

    Here’s is what we will cover:

    1. When to Collect Human Feedback
    2. Types of Feedback for AI Products
    3. How to Collect Human Feedback for AI Products?
    4. Integrating Feedback into the User Experience Learning
    5. Leveraging Structured Feedback Platforms

    When to Collect AI Human Feedback

    AI products benefit from human input throughout their lifecycle. From the earliest data collection stages to long after launch, strategic feedback can make the difference between a failing AI and a product that truly delights users. Below are key phases when collecting human feedback is especially valuable:

    During Training Data Curation

    Early on, humans can help curate and generate the training data that AI models learn from. This can include collecting real user behavior data or annotating special datasets.

    For example, a pet-tech company might need unique images to train a computer vision model. In one case, Iams worked with BetaTesting to gather high-quality photos and videos of dog nose prints from a wide range of breeds and lighting scenarios. This data helped improve the accuracy of their AI-powered pet identification app designed to reunite lost dogs with their owners.

    By recruiting the right people to supply or label data (like those dog nose images), the training dataset becomes richer and more relevant. Human curation and annotation at this stage ensures the model starts learning from accurate examples rather than raw, unvetted data provided by non-experts.

    During Model Evaluation

    Once an AI model is trained, we need to know how well it actually works for real users. Automated metrics (accuracy, loss, etc.) only tell part of the story. Human evaluators are crucial for judging subjective qualities like usefulness, clarity, or bias in model outputs. As one research paper puts it, 

    “Human evaluation is indispensable and inevitable for assessing the quality of texts generated by machine learning models or written by humans.”

    In practice, this might mean having people rate chatbot answers for correctness and tone, or run usability tests on an AI feature to see if it meets user needs. Human input during evaluation catches issues that pure metrics miss – for instance, an image recognition model might score well in lab tests but could still output results that are obviously irrelevant or offensive to users.

    By involving actual people to review and score the AI’s performance, product teams can identify these shortcomings. The model can then be adjusted before it reaches a wider audience.

    During Model Fine-Tuning

    Initial training often isn’t the end of teaching an AI. Fine-tuning with human feedback can align a model with what users prefer or expect. A prominent technique is Reinforcement Learning from Human Feedback (RLHF), where human preferences directly shape the model’s behavior. The primary advantage of the RLHF approach is that it “capture[s] nuance and subjectivity by using positive human feedback in lieu of formally defined objectives.”

    In other words, people can tell the AI what’s a “good” or “bad” output in complex situations where there’s no simple right answer. For example, fine-tuning a language model with RLHF might involve showing it several responses to a user query and having human reviewers rank them. The model learns from these rankings to generate more preferred answers over time.

    This stage is key for aligning AI with human values, polishing its manners, and reducing harmful outputs. Even supervised fine-tuning (having humans provide the correct responses for the model to mimic) is a form of guided improvement based on human insight.

    For Pre-Launch User Testing

    Before rolling out an AI-driven product or feature publicly, it’s wise to get feedback from a controlled group of humans. Beta tests, pilot programs, or “trusted tester” groups allow you to see how the AI performs with real users in realistic scenarios – and gather their impressions. This kind of early feedback can prevent public debacles.

    Recall when Google hastily demoed its Bard chatbot and it made a factual error? They quickly emphasized a phased testing approach after that misstep. 

    “This highlights the importance of a rigorous testing process… We’ll combine external feedback with our own internal testing to make sure Bard’s responses meet a high bar for quality, safety and groundedness in real-world information.” – Jane Park, Google spokesperson

    The idea is to catch problems early – be it model errors or UI confusion – by having humans use the AI in a beta context. Pre-launch feedback helps teams address any issues of accuracy, fairness, or usability before wider release, ultimately saving the product from negative user reactions and press.

    For Ongoing Feedback in Production

    Human feedback shouldn’t stop once the product is live. In production, continuous feedback loops help the AI stay effective and responsive to user needs. Real users will inevitably push the AI into new territory or encounter edge cases. By giving them easy ways to provide feedback, you can catch issues and iterate.

    For instance, many AI chat services have a thumbs-up/down or “Was this helpful?” prompt after answers – these signals go back into improving the model over time. Similarly, usage analytics can reveal where users get frustrated (e.g. repeating a query or abandoning a conversation). Even without explicit input, monitoring implicit signals (more on that below) like the length of user sessions or dropout rates can hint at satisfaction levels.

    The key is treating an AI product as a continually learning system: using live feedback data to fix issues, update training, or roll out improvements. Ongoing human feedback ensures the AI doesn’t grow stale or drift away from what users actually want, long after launch day.

    Check it out: We have a full article on AI Product Validation With Beta Testing

    Types of Feedback for AI Products

    Not all feedback is alike – it comes in different forms, each offering unique insights. AI product teams should think broadly about what counts as “feedback,” from a star rating to a silent pause. Below are several types of feedback that can inform AI systems:

    Task Success Rate:  At the end of the day, one of the most telling measures of an AI product’s effectiveness is whether users can achieve their goals with it. In user experience terms, this is often called task success or completion rate. Did the user accomplish what they set out to do with the help of the AI? For instance, if the AI is a scheduling assistant, did it successfully book a meeting for the user? If it’s a medical symptom checker, did the user get appropriate advice or a doctor’s appointment?

    Tracking task success may require defining what a “successful outcome” is for your specific product and possibly asking the user (an explicit post-task survey: “Were you able to do X?”). It can also be inferred in some cases (if the next action after using the AI is the user calling support, perhaps the AI failed). According to usability experts, “success rates are easy to collect and a very telling statistic. After all, if users can’t accomplish their target task, all else is irrelevant. As quoted in this article from NN/g, User success is the bottom line of usability. . In other words, fancy features and high engagement mean little if the AI isn’t actually helping users get stuff done.

    Thus, measuring task success (e.g. percentage of conversations where the user’s question was answered to their satisfaction, or percentage of AI-driven e-commerce searches that ended in a purchase) provides concrete feedback on the AI’s utility. Low success rates flag a need to improve the AI’s capabilities or the product flow around it. High success rates, especially paired with positive qualitative feedback, are strong validation that the AI is meeting user needs.

    Explicit vs. Implicit Feedback: These are two fundamental categories: 

    Explicit feedback refers to direct, intentional user input – like ratings, reviews, or survey responses – where users explicitly state preferences.

    Implicit feedback, on the other hand, is inferred from user actions, such as clicks, purchase history, or time spent viewing content.

    In short, explicit feedback is an intentional signal (for example, a user gives a chatbot answer 4 out of 5 stars or writes “This was helpful”), whereas implicit feedback is gathered by observing user behavior (for example, the user keeps using the chatbot for 10 minutes, which implies it was engaging). Both types are valuable.

    Explicit feedback is precise but often sparse (not everyone rates or comments), while implicit feedback is abundant but must be interpreted carefully. A classic implicit signal is how a user interacts with content: Platforms like YouTube or Netflix monitor which videos users start, skip, or rewatch. If a user watches 90% of a movie, this strongly suggests they enjoyed it, while abandoning a video after 2 minutes might indicate disinterest. Here, the length of engagement (90% vs. 2 minutes) is taken as feedback about content quality or relevance.

    AI products should leverage both kinds of feedback – explicit when you can get it, and implicit gleaned from normal usage patterns.

    Natural Language Feedback: Sometimes users will literally tell your AI what they think, in plain words. For example, a user might type to a chatbot, “That’s not what I asked for,” or say to a voice assistant, “No, that’s wrong.” This free-form feedback is gold. It’s explicit, but it’s not in the form of a structured rating – it’s in the user’s own words.

    Natural language feedback can highlight misunderstandings (“I meant Paris, Texas, not Paris, France”), express frustration (“You’re not making sense”), or give suggestions (“Can you show me more options?”). Modern AI systems can be designed to parse such input: a chatbot could detect phrases like “not what I asked” as a signal it provided an irrelevant answer, triggering a corrective response or at least logging the incident for developers. Unlike hitting a thumbs-down button, verbal feedback often contains specifics about whythe user is dissatisfied or what they expected.

    Capturing and analyzing these comments can guide both immediate fixes (e.g. the AI apologizes or tries again) and longer-term improvements (e.g. adjusting the model or content based on common complaints).

    Indicators of User Disengagement:  Not all feedback is explicit; often, inaction or avoidance is a feedback signal. If users stop interacting with your AI or opt out of using it, something might be wrong. For instance, in a chat interface, if the user suddenly stops responding or closes the app after the AI’s answer, that could indicate the answer wasn’t helpful or the user got frustrated.

    High dropout rates at a certain step in an AI-driven onboarding flow signal a poor experience. Skipping behavior is another telltale sign: consider a music streaming service – if a listener consistently skips a song after a few seconds, it’s a strong implicit signal they don’t like it. Similarly, if users of a recommendation system frequently hit “next” or ignore certain suggestions, the AI may not be meeting their needs.

    These disengagement cues (rapid skipping, closing the session, long periods of inactivity) serve as negative feedback that the AI or content isn’t satisfying. The challenge is interpreting them correctly. One user might leave because they got what they needed quickly (a good thing), whereas another leaves out of frustration. Context is key, but overall patterns of disengagement are a red flag that should feed back into product tweaks or model retraining.

    Complaint Mechanisms: When an AI system does something really off-base – say it produces inappropriate content, makes a serious error, or crashes – users need a way to complain or flag the issue.

    A well-designed AI product includes feedback channels for complaints, such as a “Report this result” link, an option to contact support, or forms to submit bug reports. These mechanisms gather crucial feedback on failures and harm. For example, a generative AI image app might include a button to report outputs that are violent or biased. Those reports alert the team to content that violates guidelines and also act as training data – the model can learn from what not to do. Complaint feedback is typically explicit (the user actively submits it) and often comes with high urgency.

    It’s important to make complaining easy; if users can’t tell you something went wrong, you’ll never know to fix it. Moreover, having a complaint channel can make users feel heard and increase trust, even if they never use it. In the backend, every complaint or flagged output should be reviewed. Common issues might prompt an immediate patch or an update to the AI’s training. For instance, if multiple users of a language model flag responses as offensive, developers might refine the model’s filtering or training on sensitive topics.

    Complaints are painful to get, but they’re direct feedback on the worst-case interactions – exactly the ones you want to minimize.

    Features for Re-requests or Regeneration: Many AI products allow the user to say “Try again” in some fashion. Think of the “Regenerate response” feature in ChatGPT or a voice assistant saying, “Would you like me to rephrase that?” These features serve two purposes: they give users control to correct unsatisfactory outcomes, and they signal to the system that the last attempt missed the mark.

    A user hitting the retry button is implicit feedback that the previous output wasn’t good enough. Some systems might even explicitly ask why: e.g., after hitting “Regenerate,” a prompt could appear like “What was wrong with the last answer?” to gather explicit feedback. Even without that, the act of re-requesting content helps developers see where the AI frequently fails. For example, if 30% of users are regenerating answers to a certain type of question, that’s a clear area for model improvement.

    Similarly, an e-commerce recommendation carousel might have a “Show me more” button – if clicked often, it implies the initial recommendations weren’t satisfactory. Designing your AI interface to include safe fallbacks (retry, refine search, ask a human, etc.) both improves user experience and produces useful feedback data. Over time, you might analyze regenerate rates as a quality metric (lower is better) and track if changes to the AI reduce the need for users to ask twice.

    User Sentiment and Emotional Cues: Humans express how they feel about an AI’s performance not just through words, but through tone of voice, facial expressions, and other cues. Advanced AI products, especially voice and vision interfaces, can attempt to read these signals.

    For instance, an AI customer service agent on a call might detect the customer’s tone becoming angry or frustrated and escalate to a human or adapt its responses. An AI in a car might use a camera to notice if the driver looks confused or upset after the GPS gives a direction, treating that as a sign to clarify. Text sentiment analysis is a simpler form: if a user types “Ugh, this is useless,” the sentiment is clearly negative. All these signals of user sentiment can be looped back into improving the AI’s responses.

    They are implicit (the user isn’t explicitly saying “feedback: I’m frustrated” in a form), but modern multimodal AI can infer them. However, using sentiment as feedback must be done carefully and with privacy in mind – not every furrowed brow means dissatisfaction with the AI. Still, sentiment indicators, when clear, are powerful feedback on how the AI is impacting user experience emotionally, not just functionally.

    Engagement Metrics: The product analytics for your AI feature can be viewed as a giant pool of implicit feedback. Metrics like session length, number of turns in a conversation, frequency of use, and feature adoption rates all tell a story. If users are spending a long time chatting with your AI or asking it many follow-up questions, that could mean it’s engaging and useful (or possibly that it’s slow to help, so context matters).

    Generally, higher engagement and repeated use are positive signs for consumer AI products – they indicate users find value. Conversely, low usage or short sessions might indicate the AI is not useful enough or has usability issues. For example, if an AI writing assistant is only used for 30 seconds on average, maybe it’s not integrating well into users’ workflow.

    Engagement metrics often feed into key performance indicators (KPIs) that teams set. They also allow for A/B testing feedback: you can release version A and B of an AI model to different user groups and see which drives longer interactions or higher click-through, treating those numbers as feedback on which model is better. One caution: more engagement isn’t always strictly better – in some applications like healthcare, you might want the AI to help users quickly and efficiently (short sessions mean it solved the problem fast).

    So it’s important to tie engagement metrics to task success or satisfaction measures to interpret them correctly. Nonetheless, engagement data at scale can highlight where an AI product delights users (high uptake, long use, strong retention) versus where it might be falling flat.

    How to Collect Human Feedback for AI Products?

    Knowing you need feedback and actually gathering it are two different challenges. Collecting human feedback in AI development requires thoughtful mechanisms that vary by development stage and context. It also means embedding feedback tools into your product experience so that giving feedback is as seamless as using the product itself.

    Finally, leveraging structured platforms or communities can supercharge your feedback collection by providing access to large pools of testers. Let’s break down how to collect feedback effectively:

    Feedback Mechanisms at Different Development Stages

    The way you gather feedback will differ depending on whether you’re training a model, evaluating it, fine-tuning, testing pre-launch, or monitoring a live system. Each stage calls for tailored tactics:

    • Data curation stage: Here you might use crowdsourcing or managed data collection. For example, if you need a dataset of spoken commands to train a voice AI, you could recruit users (perhaps through a service) to record phrases and then rate the accuracy of transcriptions.

      If you’re labeling data, you might employ annotation platforms where humans label images or text. At this stage, feedback collection is often about getting inputs(labeled data, example corrections) rather than opinions. Think of it as asking humans: “What is this? Is this correct?” and feeding those answers into model training.
    • Model evaluation stage: Now the model exists and you need humans to assess outputs. Common mechanisms include structured reviews (like having human judges score AI outputs for correctness or quality), side-by-side comparisons (which output did the human prefer?), and user testing sessions. You might leverage internal staff or external beta users to try tasks with the AI and report issues.

      Surveys and interviews after using the AI can gather qualitative feedback on how well it performs. If you have the resources, formal usability testing (observing users trying to complete tasks with the AI) provides rich insight. The goal here is to collect feedback on the model’s performance: “Did it do a good job? Where did it fail?”
    • Fine-tuning stage: When refining the model with human feedback (like RLHF), continuous rating loops are key. One method is to deploy the model in a constrained setting and have labelers or beta users rate each response or choose the better of two responses. This can be done using simple interfaces – for instance, a web app where a tester sees a prompt and two AI answers and clicks which is better. 

      A prime illustration of this can be observed in ChatGPT, where users can rate the AI’s outputs using a thumbs-up or thumbs-down mechanism. This collective feedback holds immense value in enhancing the reward model, providing direct insights into human preferences. In other words, even after initial training, you actively solicit user ratings on outputs and feed those into a fine-tuning loop.

      If you’re running a closed beta, you might prompt testers to mark each interaction as good or bad. Fine-tuning often blurs into early deployment, as the AI learns from a controlled user group.
    • Pre-launch testing stage: At this point, you likely have a more polished product and are testing in real-world conditions with a limited audience. Beta tests are a prime tool. You might recruit a few hundred users representative of your target demographic to use the AI feature over a couple of weeks. Provide them an easy way to give feedback – in-app forms, a forum, or scheduled feedback sessions.

      Many products include a quick feedback widget (like a bug report or suggestion form) directly within the beta version. For example, an AI chatbot beta might have a small “Send feedback” button in the corner of the chat. Testers are often asked to complete certain tasks and then fill out surveys on their experience.

      This stage is less about scoring individual AI responses (you’ve hopefully ironed out major issues by now) and more about holistic feedback: Did the AI integrate well? Did it actually solve your problem? Were there any surprises or errors? This is where you catch things like “The AI’s tone felt too formal” or “It struggled with my regional accent.”

      Structured programs with recruited testers can yield high-quality feedback because testers know their input is valued. Using a dedicated community or platform for beta testing can simplify this process.
    • Production stage: Once the AI is live to all users, you need ongoing, scalable feedback mechanisms. It’s impractical to personally talk to every user, so the product itself must encourage feedback. Common methods include: built-in rating prompts (e.g. after a chatbot interaction: “👍 or 👎?”), periodic user satisfaction surveys (perhaps emailed or in-app after certain interactions), and passive feedback collection through analytics (as discussed, monitoring usage patterns). Additionally, you might maintain a user community or support channel where people can report issues or suggestions.

      Some companies use pop-ups like “How was this answer?” after a query, or have a help center where users can submit feedback tickets. Another approach is to occasionally ask users to opt-in to more in-depth studies – for instance, “Help us improve our AI – take a 2-minute survey about your experience.”

      Finally, don’t forget A/B testing and experiments: by releasing tweaks to small percentages of users and measuring outcomes, you gather feedback in the form of behavioral data on what works better. In production, the key is to make feedback collection continuous but not annoying. The mechanisms should run in the background or as a natural part of user interaction.

    Did you know that Fine-tuning is one of the top 10 AI terms startups should know about? Check out the rest here is this article: Top 10 AI Terms Startups Need to Know

    Integrating Feedback into the User Experience Learning

    No matter the stage, one principle is paramount: make giving feedback a seamless part of using the AI product. Users are more likely to provide input if it’s easy, contextual, and doesn’t feel like a chore. PulseLabs notes:

    “An effective feedback system should feel like a natural extension of the user experience. For example, in-app prompts for rating responses, options to flag errors, and targeted surveys can gather valuable insights without disrupting workflow”

    This means if a user is chatting with an AI assistant, a non-intrusive thumbs-up/down icon can be present right by each answer – if they click it, perhaps a text box appears asking for optional details, then disappears. If the AI is part of a mobile app, maybe shaking the phone or a two-finger tap could trigger a feedback screen (some apps do this for bug reporting). The idea is to capture feedback at the moment when the user has the thought or emotion about the AI’s performance.

    A good design is to place feedback entry points right where they’re needed – a “Was this correct?” yes/no next to an AI-transcribed sentence, or a little sad face/happy face at the end of a voice interaction on a smart speaker.

    Importantly, integrating feedback shouldn’t burden or annoy the user. We must respect the user’s primary task. If they’re asking an AI for help, they don’t want to fill out a long form every time. So we aim for lightweight inputs: one-click ratings, implicit signals, or the occasional quick question. Some products implement feedback over time rather than every interaction – for instance, after every 5th use, it might ask “How are we doing?” This spreads out the requests. Also, integrating feedback means closing the loop.

    Whenever possible, acknowledge feedback within the UX. If a user flags an AI output as wrong, the system might reply with “Thanks, I’ve noted that issue” or even better, attempt to correct itself. When beta testers gave feedback, savvy companies will respond in release notes or emails: “You spoke, we listened – here’s what we changed.” This encourages users to keep giving input because they see it has an effect.

    One clever example of integration is ChatGPT’s conversational feedback. As users chat, they can provide a thumbs-down and even explain why, all in the same interface, without breaking flow. The model might not instantly change, but OpenAI collects that and even uses some of it to improve future versions. Another example is a voice assistant that listens not just to commands but to hesitation or repetition – if you ask twice, it might say “Let me try phrasing that differently.” That’s the AI using the feedback in real-time to improve UX.

    Ultimately, feedback tools should be part of the product’s DNA, not an afterthought. When done right, users sometimes don’t even realize they’re providing feedback – it feels like just another natural interaction with the system, yet those interactions feed the AI improvement pipeline behind the scenes.

    Leveraging Structured Feedback Platforms

    Building your own feedback collection process can be resource-intensive. This is where structured feedback platforms and communities come in handy. Services like BetaTesting (among others) specialize in connecting products with real users and managing the feedback process. At BetaTesting, we maintain a large community of vetted beta testers and provide tools for distributing test builds, collecting survey responses, bug reports, and usage data. As a result, product teams can get concentrated feedback from a target demographic quickly, without having to recruit and coordinate testers from scratch. This kind of platform is especially useful during pre-launch and fine-tuning stages. You can specify the type of testers you need (e.g. by demographic or device type) and what tasks you want them to do, then receive structured results.

    One primary example of using such a platform for AI feedback is in data collection for model improvement. Recall the earlier mention of Iams and the dog nose prints. That effort was facilitated by BetaTesting’s network: 

    Faurecia partnered with BetaTesting to collect real-world, in-car images from hundreds of users across different locations and conditions. These photos were used to train and improve Faurecia’s AI systems for better object recognition and environment detection in vehicles.

    In this case, BetaTesting provided the reach and organization to gather a diverse dataset (images from various cars, geographies, lighting) which a single company might struggle to assemble on its own. The same platform also gathered feedback on how the AI performed with those images, effectively crowd-sourcing the evaluation and data enrichment process.

    Structured platforms often offer a dashboard to analyze feedback, which can be a huge time-saver. They might categorize issues, highlight common suggestions, or even provide benchmark scores (e.g., average satisfaction rating for your AI vs. industry). For AI products, some platforms now focus on AI-specific feedback, like having testers interact with a chatbot and then answer targeted questions about its coherence, or collecting voice samples to improve speech models.

    Using a platform is not a substitute for listening to your own users in the wild, but it’s a powerful supplement. It’s like wind-tunnel testing for AI: you can simulate real usage with a friendly audience and get detailed feedback reports. Particularly for startups and small teams, these services make it feasible to do thorough beta tests and iterative improvement without a dedicated in-house research team.

    Another avenue is leveraging communities (Reddit, Discord, etc.) where enthusiasts give feedback freely. Many AI projects, especially open-source or academic ones, have public Discord servers where users share feedback and the developers actively gather that input. While this approach can provide very passionate feedback, it may not cover the breadth of average users that a more structured beta test would. Hence, a mix of both can work: use a platform for broad, structured input and maintain a community channel for continuous, organic feedback.

    In summary, collecting human feedback for AI products is an ongoing, multi-faceted effort. It ranges from the invisible (logging a user’s pauses) to the very visible (asking a user to rate an answer). Smart AI product teams plan for feedback at every stage, use the right tool for the job (be it an in-app prompt or a full beta program), and treat user feedback not as a one-time checkbox but as a continuous source of improvement. By respecting users’ voices and systematically learning from them, we make our AI products not only smarter but also more user-centered and successful.

    Check it out: We have a full article on AI-Powered User Research: Fraud, Quality & Ethical Questions

    Conclusion

    Human feedback is the secret sauce that turns a merely clever AI into a truly useful product. Knowing when to ask for input, what kind of feedback to look for, and how to gather it efficiently can dramatically improve your AI’s performance and user satisfaction.

    Whether you’re curating training data, fine-tuning a model with preference data, or tweaking a live system based on user behavior, remember that every piece of feedback is a gift. It represents a real person’s experience and insight. As we’ve seen, successful AI products like ChatGPT actively incorporate feedback loops, and tools like BetaTesting make it easier to harness collective input.

    The takeaway for product managers, researchers, engineers, and entrepreneurs is clear: keep humans in the loop. By continually learning from your users, your AI will not only get smarter – it will stay aligned with what people actually need and value. In the fast-evolving world of AI, that alignment is what separates the products that fizzle from those that flourish.

    Use human feedback wisely, and you’ll be well on your way to building AI solutions that improve continuously and delight consistently.

    Have questions? Book a call in our call calendar.

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  • How To Collect User Feedback & What To Do With It.

    In today’s fast-paced market, delivering products that exceed customer expectations is critical.

    Beta testing provides a valuable opportunity to collect real-world feedback from real users, helping companies refine and enhance their products before launching new products or new features.

    Collecting and incorporating beta testing feedback effectively can significantly improve your product, reduce development costs, and increase user satisfaction. Here’s how to systematically collect and integrate beta feedback into your product development cycle, supported by real-world examples from industry leaders.

    Here’s what we will explore:

    1. Collect and Understand Feedback (ideally with the help of AI)
    2. Prioritize the Feedback
    3. Integrate Feedback into Development Sprints
    4. Validate Implemented Feedback
    5. Communicate Changes and Celebrate Contributions
    6. Ongoing Iteration and Continuous Improvement

    Collect & Understand Feedback (ideally with the help of AI)

    Effective beta testing hinges on gathering feedback that is not only abundant but also clear, actionable, and well-organized. To achieve this, consider the following best practices:

    • Surveys and Feedback Forms: Design your feedback collection tools to guide testers through specific areas of interest. Utilize a mix of question types, such as multiple-choice for quantitative data and open-ended questions for qualitative insights.
    • Video and Audio: Modern qualitative feedback often includes video and audio (e.g. selfie videos, unboxing, screen recordings, conversations with AI bots, etc).
    • Encourage Detailed Context: Prompt testers to provide context for their feedback. Understanding the environment in which an issue occurred can be invaluable for reproducing and resolving problems.
    • Categorize Feedback: Implement a system to categorize feedback based on themes or severity. This organization aids in identifying patterns and prioritizing responses.

    All of the above are made easier due to recent advances in AI.

    Read our article to learn how AI is currently used in user research.

    By implementing these strategies, teams can transform raw feedback into a structured format that is easier to analyze and act upon, ultimately leading to more effective product improvements.

    At BetaTesting, we got you covered. We provide the platform to make it easy to collect and understand feedback in various ways (primarily: video, surveys, and bugs) and other supportive capabilities to design and execute beta tests that can collect clear, actionable, insightful, and well-organized feedback.

    Check it out: We have a full article on The Psychology of Beta Testers: What Drives Participation?

    Prioritize the Feedback

    Collecting beta feedback is only half the battle – prioritizing it effectively is where the real strategic value lies. With dozens (or even hundreds) of insights pouring in from testers, product teams need a clear process to separate signal from noise and determine what should be addressed, deferred, or tracked for later.

    A strong prioritization system ensures that the most critical improvements, those that directly affect product quality and user satisfaction are acted upon swiftly. Here’s how to do it well:

    Core Prioritization Criteria

    When triaging feedback, evaluate it across several key dimensions:

    • Frequency – How many testers reported the same issue? Repetition signals a pattern that could impact a broad swath of users.
    • Impact – How significantly does the issue affect user experience? A minor visual bug might be low priority, while a broken core workflow could be urgent.
    • Feasibility – How difficult is it to address? Balance the value of the improvement with the effort and resources required to implement it.
    • Strategic Alignment – Does the feedback align with the product’s current goals, roadmap, or user segment focus?

    This method ensures you’re not just reacting to noise but making product decisions grounded in value and vision.

    How to Implement a Prioritization System

    To emulate a structured approach, consider these tactics:

    • Tag and categorize feedback: Use tags such as “critical bug,” “minor issue,” “feature request,” or “UX confusion.” This helps product teams spot clusters quickly.
    • Create a prioritization matrix: Plot feedback on a 2×2 matrix, impact vs. effort. Tackle high-impact, low-effort items first (your “quick wins”), and flag high-impact/high-effort items for planning in future sprints.
    • Involve cross-functional teams: Bring in engineers, designers, and marketers to discuss the tradeoffs of each item. What’s easy to fix may be a huge win, and what’s hard to fix may be worth deferring.
    • Communicate decisions: If you’re closing a piece of feedback without action, let testers know why. Transparency helps maintain goodwill and future engagement.

    By prioritizing feedback intelligently, you not only improve the product, you also demonstrate respect for your testers’ time and insight. It turns passive users into ongoing collaborators and ensures your team is always solving the right problems.

    Integrate Feedback into Development Sprints

    Incorporating user feedback into your agile processes is crucial for delivering products that truly meet user needs. To ensure that valuable insights from beta testing are not overlooked, it’s essential to systematically translate this feedback into actionable tasks within your development sprints.

    At Atlassian, this practice is integral to their workflow. Sherif Mansour, Principal Product Manager at Atlassian, emphasizes the importance of aligning feedback with sprint goals:

    “Your team needs to have a shared understanding of the customer value each sprint will deliver (or enable you to). Some teams incorporate this in their sprint goals. If you’ve agreed on the value and the outcome, the individual backlog prioritization should fall into place.”

    By embedding feedback into sprint planning sessions, teams can ensure that user suggestions directly influence development priorities. This approach not only enhances the relevance of the product but also fosters a culture of continuous improvement and responsiveness to user needs.

    To effectively integrate feedback:

    • Collect and Categorize: Gather feedback from various channels and categorize it based on themes or features.
    • Prioritize: Assess the impact and feasibility of each feedback item to prioritize them effectively.
    • Translate into Tasks: Convert prioritized feedback into user stories or tasks within your project management tool.
    • Align with Sprint Goals: Ensure that these tasks align with the objectives of upcoming sprints.
    • Communicate: Keep stakeholders informed about how their feedback is being addressed.

    By following these steps, teams can create a structured approach to incorporating feedback, leading to more user-centric products and a more engaged user base.

    Validate Implemented Feedback

    After integrating beta feedback into your product, it’s crucial to conduct validation sessions or follow-up tests with your beta testers. This step ensures that the improvements meet user expectations and effectively resolve the identified issues. Engaging with testers post-implementation helps confirm that the changes have had the desired impact and allows for the identification of any remaining concerns.

    To effectively validate implemented feedback:

    • Re-engage Beta Testers: Invite original beta testers to assess the changes, providing them with clear instructions on what to focus on.
    • Structured Feedback Collection: Use surveys or interviews to gather detailed feedback on the specific changes made.
    • Monitor Usage Metrics: Analyze user behavior and performance metrics to objectively assess the impact of the implemented changes.
    • Iterative Improvements: Be prepared to make further adjustments based on the validation feedback to fine-tune the product.

    By systematically validating implemented feedback, you ensure that your product evolves in alignment with user needs and expectations, ultimately leading to higher satisfaction and success in the market.

    Communicate Changes and Celebrate Contributions

    Transparency is key in fostering trust and engagement with your beta testers. After integrating their feedback, it’s essential to inform them about the changes made and acknowledge their contributions. This not only validates their efforts but also encourages continued participation and advocacy.

    Best Practices:

    • Detailed Release Notes: Clearly outline the updates made, specifying which changes were driven by user feedback. This helps testers see the direct impact of their input.
    • Personalized Communication: Reach out to testers individually or in groups to thank them for specific suggestions that led to improvements.
    • Public Acknowledgment: Highlight top contributors in newsletters, blogs, or social media to recognize their valuable input.
    • Incentives and Rewards: Offer small tokens of appreciation, such as gift cards or exclusive access to new features, to show gratitude.

    By implementing these practices, you create a positive feedback loop that not only improves your product but also builds a community of dedicated users.

    Check it out: We have a full article on Giving Incentives for Beta Testing & User Research

    Ongoing Iteration and Continuous Improvement

    Beta testing should be viewed as an ongoing process rather than a one-time event. Continuous engagement with users allows for regular feedback, leading to iterative improvements that keep your product aligned with user needs and market trends.

    Strategies for Continuous Improvement:

    • Regular Feedback Cycles: Schedule periodic check-ins with users to gather fresh insights and identify new areas for enhancement.
    • Agile Development Integration: Incorporate feedback into your agile workflows to ensure timely implementation of user suggestions.
    • Data-Driven Decisions: Use analytics to monitor user behavior and identify patterns that can inform future updates.
    • Community Building: Foster a community where users feel comfortable sharing feedback and suggestions, creating a collaborative environment for product development.

    By embracing a culture of continuous improvement, you ensure that your product evolves in step with user expectations, leading to sustained success and user satisfaction.

    Seeking only positive feedback and cheerleaders is one of the mistakes companies make. We explore them in depth here in this article, Top 5 Mistakes Companies Make In Beta Testing (And How to Avoid Them)

    Conclusion

    Successfully managing beta feedback isn’t just about collecting bug reports, it’s about closing the loop. When companies gather actionable insights, prioritize them thoughtfully, and integrate them into agile workflows, they don’t just improve their product, they build trust, loyalty, and long-term user engagement.

    The most effective teams treat beta testers as partners, not just participants. They validate changes with follow-up sessions, communicate updates transparently, and celebrate tester contributions openly. This turns casual users into invested advocates who are more likely to stick around, spread the word, and continue offering valuable feedback.

    Whether you’re a startup launching your first app or a mature product team refining your roadmap, the formula is clear: structured feedback + implementation + open communication = better products and stronger communities. When beta testing is done right, everyone wins.

    Have questions? Book a call in our call calendar.

    Book a Call or Demo
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  • Building a Beta Tester Community: Strategies for Long-Term Engagement

    In today’s fast-paced and competitive digital market, user feedback is an invaluable asset. Beta testing serves as the critical bridge between product development and market launch, enabling real people to interact with products and offer practical insights.

    However, beyond simple pre-launch testing lies an even greater opportunity: a dedicated beta tester community for ongoing testing and engagement. By carefully nurturing and maintaining such a community, product teams can achieve continuous improvement, enhanced user satisfaction, and sustained product success.

    Here’s is what we will explore:

    1. The Importance of a Beta Tester Community
    2. Laying the Foundation
    3. Strategies for Sustaining Long-Term Engagement
    4. Leveraging Technology and Platforms
    5. Challenges and Pitfalls to Avoid
    6. Case Studies and Real-World Examples

    The Importance of a Beta Tester Community

    Continuous Feedback Loop with Real Users

    One of the most substantial advantages of cultivating a beta tester community is the creation of a continuous feedback loop. A community offers direct, ongoing interaction with real users, providing consistent insights into product performance and evolving user expectations. Unlike one-off testing, a community ensures a constant flow of relevant user feedback, enabling agile, responsive, and informed product development.

    Resolving Critical Issues Before Public Release

    Beta tester communities act as an early detection system for issues that internal teams may miss. Engaged testers often catch critical bugs, usability friction, or unexpected behaviors early in the product lifecycle. By addressing these issues before they reach the broader public, companies avoid negative reviews, customer dissatisfaction, and costly post-launch fixes. Early resolutions enhance a product’s reputation for reliability and stability.

    Fostering Product Advocates

    A vibrant community of beta testers doesn’t just provide insights, they become passionate advocates of your product. Testers who see their feedback directly influence product development develop a personal stake in its success. Their enthusiasm translates naturally into authentic, influential word-of-mouth recommendations, creating organic marketing momentum that paid advertising struggles to match.

    Reducing Costs and Development Time

    Early discovery of usability issues through community-driven testing significantly reduces post-launch support burdens. Insightful, targeted feedback allows product teams to focus resources on high-impact features and necessary improvements, optimizing development efficiency. This targeted approach not only saves time but also controls development costs effectively.

    Laying the Foundation

    Build Your Community

    Generating Interest – To build a robust beta tester community, begin by generating excitement around your product. Engage your existing customers, leverage social media, industry forums, or targeted newsletters to announce beta opportunities. Clearly articulate the benefits of participation, such as exclusive early access, direct influence on product features, and recognition as a valued contributor.

    Inviting the Right People – Quality matters more than quantity. Invite users who reflect your intended customer base, those enthusiastic about your product and capable of providing clear, constructive feedback. Consider implementing screening questionnaires or short interviews to identify testers who demonstrate commitment, effective communication skills, and genuine enthusiasm for your product’s domain.

    Managing the Community – Effective community management is crucial. Assign dedicated personnel who actively engage with testers, provide timely responses, and foster an open and collaborative environment. Transparent and proactive management builds trust and encourages ongoing participation, turning occasional testers into long-term, committed community members.

    Set Clear Expectations and Guidelines

    Set clear expectations from the outset. Clearly communicate the scope of tests, feedback requirements, and timelines. Providing structured guidelines ensures testers understand their roles, reduces confusion, and results in more relevant, actionable feedback.

    Design an Easy Onboarding Process

    An easy and seamless onboarding process significantly improves tester participation and retention. Provide clear instructions, necessary resources, and responsive support channels. Testers who can quickly and painlessly get started are more likely to stay engaged over time.

    Strategies for Sustaining Long-Term Engagement

    Communication and Transparency

    Transparent, regular communication is the foundation of sustained engagement. Provide frequent updates on product improvements, clearly demonstrating how tester feedback shapes product development. This openness builds trust, encourages active participation, and fosters a sense of meaningful contribution among testers.

    Recognition and Rewards

    Acknowledging tester efforts goes a long way toward sustaining engagement. Celebrate their contributions publicly, offer exclusive early access to new features, or provide tangible rewards such as gift cards or branded merchandise. Recognition signals genuine appreciation, motivating testers to remain involved long-term.

    Check it out: We have a full article on Giving Incentives for Beta Testing & User Research

    Gamification and Community Challenges

    Gamification elements, such as leaderboards, badges, or achievements can significantly boost tester enthusiasm and involvement. Friendly competitions or community challenges create a sense of camaraderie, fun, and ongoing engagement, transforming routine feedback sessions into vibrant, interactive experiences.

    Continuous Learning and Support

    Providing educational materials, such as tutorials, webinars, and FAQ resources, enriches tester experiences. Supporting their continuous learning helps them understand the product more deeply, allowing them to provide even more insightful and detailed feedback. Reliable support channels further demonstrate your commitment to tester success, maintaining high morale and sustained involvement.

    Leveraging Technology and Platforms

    Choosing the right technology and platforms is vital for managing an effective beta tester community. Dedicated beta-testing platforms such as BetaTesting streamline tester recruitment, tester management, feedback collection, and issue tracking.

    Additionally, communication tools like community forums, Discord, Slack, or in-app messaging enable smooth interactions among testers and product teams. Leveraging such technology ensures efficient communication, organized feedback, and cohesive community interactions, significantly reducing administrative burdens.

    Leverage Tools and Automation is one of the 8 tips for managing beta testers You can read the full article here: 8 Tips for Managing Beta Testers to Avoid Headaches & Maximize Engagement

    Challenges and Pitfalls to Avoid

    Building and managing a beta community isn’t without challenges. Common pitfalls include neglecting timely communication, failing to implement valuable tester feedback, and providing insufficient support.

    Avoiding these pitfalls involves clear expectations, proactive and transparent communication, rapid response to feedback, and nurturing ongoing relationships. Understanding these potential challenges and addressing them proactively helps maintain a thriving, engaged tester community.

    Check it out: We have a full article on Top 5 Mistakes Companies Make In Beta Testing (And How to Avoid Them)

    How to get started on your own

    InfoQ’s Insights on Community-Driven Testing

    InfoQ highlights that creating an engaged beta community need not involve large investments upfront. According to InfoQ, a practical approach involves initiating one off limited-time beta testing programs, then gradually transitioning towards an ongoing community-focused engagement model. As they emphasize:

    “Building a community is like building a product; you need to understand the target audience and the ultimate goal.”

    This perspective reinforces the importance of understanding your community’s needs and objectives from the outset.

    Conclusion

    A dedicated beta tester community isn’t merely a beneficial addition, it is a strategic advantage that significantly enhances product development and market positioning.

    A well-nurtured community provides continuous, actionable feedback, identifies critical issues early, and fosters enthusiastic product advocacy. It reduces costs, accelerates development timelines, and boosts long-term customer satisfaction.

    By carefully laying the foundation, employing effective engagement strategies, leveraging appropriate technological tools, and learning from successful real-world examples, startups and product teams can cultivate robust tester communities. Ultimately, this investment in community building leads to products that resonate deeply, perform exceptionally, and maintain sustained relevance and success in the marketplace.

    Have questions? Book a call in our call calendar.

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  • Top 10 AI Terms Startups Need to Know

    This article breaks down the top 10 AI terms that every startup product manager, user researcher, engineer, and entrepreneur should know.

    Artificial Intelligence (AI) is beginning to revolutionize products across industries but AI terminology is new to most of us, and can be overwhelming.

    We’ll define some of the most important terms, explain what they mean, and give practical examples of how the apply in a startup context. By the end, you’ll have a clearer grasp of key AI concepts that are practically important for early-stage product development – from generative AI breakthroughs to the fundamentals of machine learning.

    Here’s are the 10 AI terms:

    1. Artificial Intelligence (AI)
    2. Machine Learning (ML)
    3. Neural Networks
    4. Deep Learning
    5. Natural Language Processing (NLP)
    6. Computer Vision (CV)
    7. Generative AI
    8. Large Language Models (LLMs)
    9. Supervised Learning
    10. Fine-Tuning

    1. Artificial Intelligence (AI)

    In simple terms, Artificial Intelligence is the broad field of computer science dedicated to creating systems that can perform tasks normally requiring human intelligence.

    AI is about making computers or machines “smart” in ways that mimic human cognitive abilities like learning, reasoning, problem-solving, and understanding language. AI is an umbrella term encompassing many subfields (like machine learning, computer vision, etc.), and it’s become a buzzword as new advances (especially since 2022) have made AI part of everyday products. Importantly, AI doesn’t mean a machine is conscious or infallible – it simply means it can handle specific tasks in a “smart” way that previously only humans could.

    Check it out: We have a full article on AI Product Validation With Beta Testing

    Let’s put it into practice, imagine a startup building an AI-based customer support tool. By incorporating AI, the tool can automatically understand incoming user questions and provide relevant answers or route the query to the right team. Here the AI system might analyze the text of questions (simulating human understanding) and make decisions on how to respond, something that would traditionally require a human support agent. Startups often say they use AI whenever their software performs a task like a human – whether it’s comprehending text, recognizing images, or making decisions faster and at scale.

    According to an IBM explanation, 

    “Any system capable of simulating human intelligence and thought processes is said to have ‘Artificial Intelligence’ (AI).” 

    In other words, if your product features a capability that lets a machine interpret or decide in a human-like way, it falls under AI.

    2. Machine Learning (ML)

    Machine Learning is a subset of AI where computers improve at tasks by learning from data rather than explicit programming. In machine learning, developers don’t hand-code every rule. Instead, they feed the system lots of examples and let it find patterns. It’s essentially teaching the computer by example.

    A definition by IBM says: 

    “Machine learning (ML) is a branch of artificial intelligence (AI) focused on enabling computers and machines to imitate the way that humans learn, to perform tasks autonomously, and to improve their performance and accuracy through experience and exposure to more data.”

    This means an ML model gets better as it sees more data – much like a person gets better at a skill with practice. Machine learning powers things like spam filters (learning to recognize junk emails by studying many examples) and recommendation engines (learning your preferences from past behavior). It’s the workhorse of modern AI, providing the techniques (algorithms) to achieve intelligent behavior by learning from datasets.

    Real world example: Consider a startup that wants to predict customer churn (which users are likely to leave the service). Using machine learning, the team can train a model on historical user data (sign-in frequency, past purchases, support tickets, etc.) where they know which users eventually canceled. The ML model will learn patterns associated with churning vs. staying. Once trained, it can predict in real-time which current customers are at risk, so the startup can take proactive steps.

    Unlike a hard-coded program with fixed rules, the ML system learns what signals matter (perhaps low engagement or specific feedback comments), and its accuracy improves as more data (examples of user behavior) come in. This adaptive learning approach is why machine learning is crucial for startups dealing with dynamic, data-rich problems – it enables smarter, data-driven product features.

    3. Neural Networks

    A Neural Network is a type of machine learning model inspired by the human brain, composed of layers of interconnected “neurons” that process data and learn to make decisions.

    Neural networks consist of virtual neurons organized in layers:

    • Input layer (taking in data)
    • Hidden layers (processing the data through weighted connections)
    • Output layer (producing a result or prediction).

    Each neuron takes input, performs a simple calculation, and passes its output to neurons in the next layer.

    Through training, the network adjusts the strength (weights) of all these connections, allowing it to learn complex patterns. A clear definition is: “An Artificial Neural Network (ANN) is a computational model inspired by the structure and functioning of the human brain.”

    These models are incredibly flexible – with enough data, a neural network can learn to translate languages, recognize faces in photos, or drive a car. Simpler ML models might look at data features one by one, but neural nets learn many layers of abstraction (e.g. in image recognition, early layers might detect edges, later layers detect object parts, final layer identifies the object).

    Learn more about: What is a Neural Network from AWS

    Example: Suppose a startup is building an app that automatically tags images uploaded by users (e.g., detecting objects or people in photos for an album). The team could use a neural network trained on millions of labeled images. During training, the network’s neurons learn to activate for certain visual patterns – some neurons in early layers react to lines or colors, middle layers might respond to shapes or textures, and final layers to whole objects like “cat” or “car.”

    After sufficient training, when a user uploads a new photo, the neural network processes the image through its layers and outputs tags like “outdoor”, “dog”, “smiling person” with confidence scores. This enables a nifty product feature: automated photo organization.

    For the startup, the power of neural networks is that they can discover patterns on their own from raw data (pixels), which is far more scalable than trying to hand-code rules for every possible image scenario.

    4. Deep Learning

    Deep Learning is a subfield of machine learning that uses multi-layered neural networks (deep neural networks) to learn complex patterns from large amounts of data.

    The term “deep” in deep learning refers to the many layers in these neural networks. A basic neural network might have one hidden layer, but deep learning models stack dozens or even hundreds of layers of neurons, which allows them to capture extremely intricate structures in data. Deep learning became practical in the last decade due to big data and more powerful computers (especially GPUs).

    A helpful definition from IBM states:

    “Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain.”

    In essence, deep learning can automatically learn features and representations from raw data. For example, given raw audio waveforms, a deep learning model can figure out low-level features (sounds), mid-level (phonetics), and high-level (words or intent) without manual feature engineering.

    This ability to learn directly from raw inputs and improve with scale is why deep learning underpins most modern AI breakthroughs – from voice assistants to self-driving car vision. However, deep models often require a lot of training data and computation. The payoff is high accuracy and the ability to tackle tasks that were previously unattainable for machines.

    Many startups leverage deep learning for tasks like natural language understanding, image recognition, or recommendation systems. For instance, a streaming video startup might use deep learning to recommend personalized content. They could train a deep neural network on user viewing histories and content attributes: the network’s layers learn abstract notions of user taste.

    Early layers might learn simple correlations (e.g., a user watches many comedies), while deeper layers infer complex patterns (perhaps the user likes “light-hearted coming-of-age” stories specifically). When a new show is added, the model can predict which segments of users will love it.

    The deep learning model improves as more users and content data are added, enabling the startup to serve increasingly accurate recommendations. This kind of deep recommendation engine is nearly impossible to achieve with manual rules, but a deep learning system can continuously learn nuanced preferences from millions of data points.

    5. Natural Language Processing (NLP)

    Natural Language Processing enables computers to understand, interpret, and generate human language (text or speech). NLP combines linguistics and machine learning so that software can work with human languages in a smart way. This includes tasks like understanding the meaning of a sentence, translating between languages, recognizing names or dates in text, summarizing documents, or holding a conversation.

    Essentially, NLP is what allows AI to go from pure numbers to words and sentences – it bridges human communication and computer processing.

    Techniques in NLP range from statistical models to deep learning (today’s best NLP systems often use deep learning, especially with large language models). NLP can be challenging because human language is messy, ambiguous, and full of context. However, progress in NLP has exploded, and modern models can achieve tasks like answering questions or detecting sentiment with impressive accuracy. For a product perspective, if your application involves text or voice from users, NLP is how you make sense of it.

    Imagine a startup that provides an AI writing assistant for marketing teams. This product might let users input a short prompt or some bullet points, and the AI will draft a well-written blog post or ad copy. Under the hood, NLP is doing the heavy lifting: the system needs to interpret the user’s prompt (e.g., understand that “social media campaign for a new coffee shop” means the tone should be friendly and the content about coffee), and then generate human-like text for the campaign.

    NLP is also crucial for startups doing things like chatbots for customer service (the bot must understand customer questions and produce helpful answers), voice-to-text transcription (converting spoken audio to written text), or analyzing survey responses to gauge customer sentiment.

    By leveraging NLP techniques, even a small startup can deploy features like language translation or sentiment analysis that would have seemed sci-fi just a few years ago. In practice, that means startups can build products where the computer actually understands user emails, chats, or voice commands instead of treating them as opaque strings of text.

    Check it out: We have a full article on AI-Powered User Research: Fraud, Quality & Ethical Questions

    6. Computer Vision (CV)

    Just as NLP helps AI deal with language, computer vision helps AI make sense of what’s in an image or video. This involves tasks like object detection (e.g., finding a pedestrian in a photo), image classification (recognizing that an image is a cat vs. a dog), face recognition, and image segmentation (outlining objects in an image).

    Computer vision combines advanced algorithms and deep learning to achieve what human vision does naturally – identifying patterns and objects in visual data. Modern computer vision often uses convolutional neural networks (CNNs) and other deep learning models specialized for images.

    These models can automatically learn to detect visual features (edges, textures, shapes) and build up to recognizing complete objects or scenes. With ample data (millions of labeled images) and training, AI vision systems can sometimes even outperform humans in certain recognition tasks (like spotting microscopic defects or scanning thousands of CCTV feeds simultaneously).

    As Micron describes, 

    “Computer vision is a field of AI that focuses on enabling computers to interpret and understand visual data from the world, such as images and videos.”

    For startups, this means your application can analyze and react to images or video – whether it’s verifying if a user uploaded a valid ID, counting inventory from a shelf photo, or powering the “try-on” AR feature in an e-commerce app – all thanks to computer vision techniques.

    Real world example: Consider a startup working on an AI-powered quality inspection system for manufacturing. Traditionally, human inspectors look at products (like circuit boards or smartphone screens) to find defects. With computer vision, the startup can train a model on images of both perfect products and defective ones.

    The AI vision system learns to spot anomalies – perhaps a scratch, misaligned component, or wrong color. On the assembly line, cameras feed images to the model which flags any defects in real time, allowing the factory to remove faulty items immediately. This dramatically speeds up quality control and reduces labor costs.

    Another example: a retail-focused startup might use computer vision in a mobile app that lets users take a photo of an item and search for similar products in an online catalog (visual search). In both cases, computer vision capabilities become a product feature – something that differentiates the startup’s offering by leveraging cameras and images.

    The key is that the AI isn’t “seeing” in the conscious way humans do, but it can analyze pixel patterns with such consistency and speed that it approximates a form of vision tailored to the task at hand.

    7. Generative AI

    Generative AI refers to AI systems that can create new content (text, images, audio, etc.) that is similar to what humans might produce, essentially generating original outputs based on patterns learned from training data.

    Unlike traditional discriminative AI (which might classify or detect something in data), generative AI actually generates something new. This could mean writing a paragraph of text that sounds like a human wrote it, creating a new image from a text description, composing music, or even designing synthetic data.

    This field has gained huge attention recently because of advances in models like OpenAI’s GPT series (for text) and image generators like DALL-E or Stable Diffusion (for images). These models are trained on vast datasets (e.g., GPT on billions of sentences, DALL-E on millions of images) and learn the statistical patterns of the content. Then, when given a prompt, they produce original content that follows those patterns.

    This encapsulates the core idea that a generative AI doesn’t just analyze data – it uses AI smarts to produce new writing, images, or other media, making it an exciting tool for startups in content-heavy arenas. The outputs aren’t just regurgitated examples from training data – they’re newly synthesized, which is why sometimes these models can even surprise us with creative or unexpected results.

    Generative AI opens possibilities for automation in content creation and design, but it also comes with challenges (like the tendency of language models to sometimes produce incorrect information, known as “hallucinations”). Still, the practical applications are vast and highly relevant to startups looking to do more with less human effort in content generation.

    Example: Many early-stage companies are already leveraging generative AI to punch above their weight. For example, a startup might offer a copywriting assistant that generates marketing content (blog posts, social media captions, product descriptions) with minimal human input. Instead of a human writer crafting each piece from scratch, the generative AI model (like GPT-4 or similar) can produce a draft that the marketing team just edits and approves. This dramatically speeds up content production.

    Another startup example: using generative AI for design prototyping, where a model generates dozens of design ideas (for logos, app layouts, or even game characters) from a simple brief. There are also startups using generative models to produce synthetic training data (e.g., generating realistic-but-fake images of people to train a vision model without privacy issues).

    These examples show how generative AI can be a force multiplier – it can create on behalf of the team, allowing startups to scale creative and development tasks in a way that was previously impossible. However, product managers need to understand the limitations too: generative models might require oversight, have biases from training data, or produce outputs that need fact-checking (especially in text).

    So, while generative AI is powerful, using it effectively in a product means knowing both its capabilities and its quirks.

    8. Large Language Models (LLMs)

    LLMs are a specific (and wildly popular) instance of generative AI focused on language. They’re called “large” because of their size – often measured in billions of parameters (weights) – which correlates with their ability to capture subtle patterns in language. Models like GPT-3, GPT-4, BERT, or Google’s PaLM are all LLMs.

    After training on everything from books to websites, an LLM can carry on a conversation, answer questions, write code, summarize documents, and more, all through a simple text prompt interface. These models use architectures like the Transformer (an innovation that made training such large models feasible by handling long-range dependencies in text effectively).

    However, they don’t truly “understand” like a human – they predict likely sequences of words based on probability. This means they can sometimes produce incorrect or nonsensical answers with great confidence (again, the hallucination issue). Despite that, their utility is enormous, and they’re getting better rapidly. For a startup, an LLM can be thought of as a powerful text-processing engine that can be integrated via an API or fine-tuned for specific needs.

    Large Language Models are very large neural network models trained on massive amounts of text, enabling them to understand language and generate human-like text. These models, such as GPT, use deep learning techniques to perform tasks like text completion, translation, summarization, and question-answering.

    A common way startups use LLMs is by integrating with services like OpenAI’s API to add smart language features. For example, a customer service platform startup might use an LLM to suggest reply drafts to support tickets. When a support request comes in, the LLM analyzes the customer’s message and generates a suggested response for the support agent, saving time.

    Another scenario: an analytics startup can offer a natural language query interface to a database – the user types a question in English (“What was our highest-selling product last month in region X?”) and the LLM interprets that and translates it into a database query or directly fetches an answer if it has been connected to the data.

    This turns natural language into an actual tool for interacting with software. Startups also fine-tune LLMs on proprietary data to create specialized chatbots (for instance, a medical advice bot fine-tuned on healthcare texts, so it speaks the language of doctors and patients).

    LLMs, being generalists, provide a flexible platform; a savvy startup can customize them to serve as content generators, conversational agents, or intelligent parsers of text. The presence of such powerful language understanding “as a service” means even a small team can add fairly advanced AI features without training a huge model from scratch – which is a game changer.

    9. Supervised Learning

    Supervised Learning is a machine learning approach where a model is trained on labeled examples, meaning each training input comes with the correct output, allowing the model to learn the relationship and make predictions on new, unlabeled data.

    Supervised learning is like learning with a teacher. We show the algorithm input-output pairs – for example, an image plus the label of what’s in the image (“cat” or “dog”), or a customer profile plus whether they clicked a promo or not – and the algorithm tunes itself to map inputs to outputs. It’s by far the most common paradigm for training AI models in industry because if you have the right labeled dataset, supervised learning tends to produce highly accurate models for classification or prediction tasks.

    A formal description from IBM states: 

    “Supervised learning is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately.”

    Essentially, the model is “supervised” by the labels: during training it makes a prediction and gets corrected by seeing the true label, gradually learning from its mistakes.

    Most classic AI use cases are supervised: spam filtering (train on emails labeled spam vs. not spam), fraud detection (transactions labeled fraudulent or legit), image recognition (photos labeled with what’s in them), etc. The downside is it requires obtaining a quality labeled dataset, which can be time-consuming or costly (think of needing thousands of hand-labeled examples). But many startups find creative ways to gather labeled data, or they rely on pre-trained models (which were originally trained in a supervised manner on big generic datasets) and then fine-tune them for their task.

    Real world example: Consider a startup offering an AI tool to vet job applications. They want to predict which applicants will perform well if hired. They could approach this with supervised learning: gather historical data of past applicants including their resumes and some outcome measure (e.g., whether they passed interviews, or their job performance rating after one year – that’s the label).

    Using this, the startup trains a model to predict performance from a resume. Each training example is a resume (input) with the known outcome (output label). Over time, the model learns which features of a resume (skills, experience, etc.) correlate with success. Once trained, it can score new resumes to help recruiters prioritize candidates.

    Another example: a fintech startup might use supervised learning to predict loan default. They train on past loans, each labeled as repaid or defaulted, so the model learns patterns indicating risk. In both cases, the key is the startup has (or acquires) a dataset with ground truth labels.

    Supervised learning then provides a powerful predictive tool that can drive product features (like automatic applicant ranking or loan risk scoring). The better the labeled data (quality and quantity), the better the model usually becomes – which is why data is often called the new oil, and why even early-stage companies put effort into data collection and labeling strategies.

    10. Fine-Tuning

    Fine-tuning has become a go-to strategy in modern AI development, especially for startups. Rather than training a complex model from scratch (which can be like reinventing the wheel, not to mention expensive in data and compute), you start with an existing model that’s already learned a lot from a general dataset, and then train it a bit more on your niche data. This adapts the model’s knowledge to your context.

    For example, you might take a large language model that’s learned general English and fine-tune it on legal documents to make a legal assistant AI. Fine-tuning is essentially a form of transfer learning – leveraging knowledge from one task for another. By fine-tuning, the model’s weights get adjusted slightly to better fit the new data, without having to start from random initialization. This typically requires much less data and compute than initial training, because the model already has a lot of useful “general understanding” built-in.

    Fine-tuning can be done for various model types (language models, vision models, etc.), and there are even specialized efficient techniques (like Low-Rank Adaptation, a.k.a. LoRA) to fine-tune huge models with minimal resources.

    For startups, fine-tuning is great because you can take open-source models or API models and give them your unique spin or proprietary knowledge. It’s how a small company can create a high-performing specialized AI without a billion-dollar budget.

    To quote IBM’s definition, “Fine-tuning in machine learning is the process of adapting a pre-trained model for a specific tasks or use cases.” This highlights that fine-tuning is all about starting from something that already works and making it work exactly for your needs. For a startup, fine-tuning can mean the difference between a one-size-fits-all AI and a bespoke solution that truly understands your users or data. It’s how you teach a big-brained AI new tricks without having to build the brain from scratch.

    Real world example: Imagine a startup that provides a virtual personal trainer app. They decide to have an AI coach that can analyze user workout videos and give feedback on form. Instead of collecting millions of workout videos and training a brand new computer vision model, the startup could take a pre-trained vision model (say one that’s trained on general human pose estimation from YouTube videos) and fine-tune it on a smaller dataset of fitness-specific videos labeled with “correct” vs “incorrect” form for each exercise.

    By fine-tuning, the model adapts to the nuances of, say, a perfect squat or plank. This dramatically lowers the barrier – maybe they only need a few thousand labeled video clips instead of millions, because the base model already understood general human movement.

    Conclusion

    Embracing AI in your product doesn’t require a PhD in machine learning, but it does help to grasp these fundamental terms and concepts. From understanding that AI is the broad goal, machine learning is the technique, neural networks and deep learning are how we achieve many modern breakthroughs, to leveraging NLP for text, computer vision for images, and generative AI for creating new content – – these concepts empower you to have informed conversations with your team and make strategic product decisions. Knowing about large language models and their quirks, the value of supervised learning with good data, and the shortcut of fine-tuning gives you a toolkit to plan AI features smartly.

    The world of AI is evolving fast (today’s hot term might be an industry standard tomorrow), but with the ten terms above, you’ll be well-equipped to navigate the landscape and build innovative products that harness the power of artificial intelligence. As always, when integrating AI, start with a clear problem to solve, use these concepts to choose the right approach, and remember to consider ethics and user experience. Happy building – may your startup’s AI journey be a successful one!

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