Reinforcement Learning from Human Feedback, or RLHF, is a way of training an AI model using human opinions about its answers. Instead of only feeding it correct text, you show people several model responses, let them rank which is best, and use those rankings to shape how the model behaves. It is a big reason modern chat assistants feel helpful rather than robotic.
Think of training a new hire. They already know the basics, but you sharpen them by reviewing their drafts and saying which version lands better with clients. Over time they internalise your taste. RLHF does the same for a model: human reviewers grade outputs, a reward model learns that pattern, and reinforcement learning pushes the main model toward the answers people scored highly.
There is a real cost to this. Human ranking is slow and expensive, so teams often have reviewers compare just two answers at a time, which is easier to judge than a number on a scale. The reward model then generalises from thousands of those small choices. Get the reward model wrong and the main model can learn to game it, sounding confident or agreeable rather than being correct.
It usually comes after a base model is already trained and lightly tuned. So RLHF is a polishing layer, not the foundation. It tends to improve tone, safety and instruction-following, and it works alongside guardrails to keep responses on the rails. But it inherits the biases of whoever did the rating, which is why the choice of reviewers matters as much as the algorithm.
At TopDevs we lean on RLHF-aligned models and add our own review steps, so the AI in a client’s product stays useful, on-brand and safe to put in front of customers.
