What Is RLHF? The Human Feedback Behind AI Models, Explained

RLHF stands for reinforcement learning from human feedback. It is one of the main reasons modern AI assistants sound helpful rather than simply plausible, and it is the technical name behind a large share of the remote 'AI trainer' and 'evaluator' roles you see listed.

The idea in one paragraph

A language model first learns from enormous amounts of text, which teaches it to produce fluent answers. Fluent is not the same as good. In RLHF, people look at the model's answers and say which ones are better, sometimes with a score and a written reason. Those judgements are used to train a second model that predicts what people prefer, and the original model is then tuned to produce answers that score well. Over many rounds, it learns to be more accurate, more useful and safer.

What the human work looks like

• Comparisons — two or more answers to the same prompt; you choose the best and explain why.
• Ratings — scoring one answer against criteria such as accuracy, completeness and tone.
• Rewrites — correcting a weak answer so it becomes an example of a good one.
• Prompt writing — creating hard questions in your field that expose the model's weaknesses.

Why experts are paid more

A general reviewer can tell when an answer is badly written. Only a lawyer can tell that a confident answer cites the wrong standard of review, and only a physician can spot a dosing error that reads perfectly well. As models improve, the mistakes that remain are the subtle ones, which is why projects increasingly recruit specialists and pay them specialist rates. The AI training category and fields like legal and healthcare show what that looks like in practice.

What makes feedback useful

The most valuable feedback is specific. "Response B is better" helps a little. "Response B is better because A applies the 2017 rule, which was superseded, and omits the exception for small employers" helps a lot. Projects measure this, and reviewers whose reasons are precise and consistent tend to be kept on and given harder tasks.

A worked example

Suppose the prompt asks: "Can my landlord keep my whole deposit for a small carpet stain?" Response A gives a confident yes with no conditions. Response B explains that deductions usually have to match actual damage beyond normal wear, that many places require an itemised list within a deadline, and that local rules vary. A legal reviewer would prefer B and say why: A states a conclusion that is wrong in most jurisdictions and omits the tenant's protections.

That single judgement, repeated across thousands of prompts by many reviewers, is what teaches the model to qualify its answers and include the details that matter.