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.
Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 2026
Frequently asked questions
- Do you need RLHF to build an AI agent?
- Not directly. RLHF is applied during model training by the model provider (Anthropic, OpenAI, etc.). As an agent builder, you benefit from RLHF through the aligned models you use. Your role is prompt engineering, RAG, and fine-tuning on top of these already-aligned models. However, understanding RLHF helps you appreciate why some models follow instructions better than others.