RLHF (Reinforcement Learning from Human Feedback)

Reinforcement Learning from Human Feedback (RLHF) is a technique for training AI models using human evaluations of response quality. Human raters compare different model outputs, and these preferences are used to fine-tune the model to generate more preferred responses. RLHF is a key technique behind the helpfulness and alignment of models like ChatGPT. For GEO, understanding RLHF explains why AI systems tend to recommend reputable, well-known brands—these are likely preferred in human evaluations.