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:
- Why the Right Audience Matters for AI Feedback?
- From Foundation Models to Expert-Tuned AI
- 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.
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