RLHF (Reinforcement Learning from Human Feedback)

A training technique where human evaluators rank AI model outputs by quality, and those rankings train a reward model that guides the AI toward more helpful, accurate, and safe responses. RLHF is the primary method used to align large language models with human preferences—transforming a base model that predicts the next token into an assistant that follows instructions, avoids harmful content, and produces genuinely useful responses. It's why modern AI agents feel helpful rather than just fluent.