For gaps that automation can't fix
300,000+ verified participants · 38+ countries · 300+ filters
200,000+ AI model engagement studies run on Prolific.
A third of ICML 2026 position papers point to the same problem: benchmarks, LLM judges, and majority-vote labels fail under audit. The best models are built with the right blend of automation and human insight.
How do humans actually behave?
Get real task execution and interaction data from a verified, diverse population. It forms the ground truth your model needs to navigate the world like a real person.
Is your model's output good, or just liked?
Preference is what someone chooses. Taste is whether it's actually good. Learn where your model's output lands with real arbiters of taste, qualified and selected for their discernment.
When the wrong call has consequences
A threshold set by the wrong sample is a risk no one can afford. Draw the signal from qualified evaluators, credential-checked Experts, and representative populations, so the call your model makes holds up.
- Verify every contributor with 100+ Protocol checks and independent Expert verification
- Recruit AI evaluators for specialized tasks, like adversarial testing and red teaming
- Embed human data directly into your model workflows with the CLI and MCP
- How Layer 6 exposes flaws in generative model evaluation metrics
"Red-teamers are doing incredible work: surfacing nuanced adversarial use cases that we wouldn't have otherwise caught."
Why AI teams choose Prolific to improve model engagement
Trusted by AI/ML developers, researchers and leading organizations across industries.
Get the right humans, exactly when you need them
Questions
Behavior is how people actually act: real task execution, interaction, and usage data that shows what humans do. Taste is whether output is good: qualified human evaluation of quality across text, image, audio, and video. Judgment is the call that has to hold up: decisions drawn from verified evaluators, credential-checked Experts, and representative populations. Most model engagement work draws on all three.
Preference tells you what someone picks between options. Taste tells you whether the output is good on its own terms, judged by people qualified to assess it. Preference data is easy to collect and easy to game. Taste requires the right evaluators, selected for their discernment.
Yes, and most do. A single workflow can capture behavioral data, taste evaluations, and expert judgment together, and you can add pillars as your needs evolve.
Every data point traces back to a real, verified, consenting person, with the screening criteria and quality checks on record. If you publish externally, you can show exactly who contributed and how they were vetted, which is why Prolific-sourced data appears in thousands of peer-reviewed papers.
For well-baselined tasks, synthetic data can perform well. But models can't reliably grade their own work where behavior, taste, and judgment are concerned: LLM judges and majority-vote labels fail under audit, and as models converge, diverse and complex human signal is what differentiates them. The strongest pipelines blend both, with humans placed where they matter most.
