Reinforcement Learning from Human Feedback (RLHF) improves AI models by using human input to guide training. Instead of relying solely on predefined rules or large datasets, it incorporates human preferences and corrections to align AI behavior with desired outcomes.
Humans review AI outputs and provide feedback, ranking or correcting responses. This input trains a reward model that guides the AI through reinforcement learning, helping it prioritize better responses. The AI iteratively improves by optimizing for human-approved behavior rather than fixed objectives.
For product managers, RLHF boosts AI usability and accuracy by tailoring models to real user needs. It reduces costly trial-and-error, enhances user trust, and supports scalable improvements without exhaustive manual programming. This leads to faster deployment, better user engagement, and improved business outcomes.