AI Glossarytechniques

Reinforcement Learning from Human Feedback (RLHF)

A training technique that aligns AI model behavior with human preferences by using human feedback to reward desired outputs and penalize undesired ones.

How It Works

RLHF is the technique that makes LLMs helpful, harmless, and honest. After initial pre-training on text data, models are further trained using human evaluators who rank outputs. A reward model learns these human preferences, and the language model is optimized to produce outputs that score highly. This is why ChatGPT feels different from a raw language model. Without RLHF, a model might generate toxic content, refuse to answer, or produce unhelpful responses. With RLHF, it learns to be conversational, follow instructions, decline harmful requests, and admit uncertainty. Anthropic uses a related technique called RLAIF (RL from AI Feedback) along with Constitutional AI. For builders, RLHF matters because it explains model behavior patterns. When a model refuses a request, that is RLHF training. When it says "I don't know" instead of hallucinating, that is RLHF. Understanding this helps you write better system prompts that work with (not against) the model's alignment training.

Common Use Cases

  • 1Understanding model behavior and limitations
  • 2Safety and alignment in AI products
  • 3Building trust in AI outputs
  • 4Designing effective system prompts

Related Terms

Large Language Model (LLM)

A neural network trained on massive text datasets that can generate, understand, and reason about human language.

Fine-Tuning

The process of further training a pre-trained AI model on your specific data to improve performance on domain-specific tasks.

Hallucination

When an AI model generates information that sounds plausible but is factually incorrect, fabricated, or not grounded in its training data.

Transfer Learning

A machine learning technique where a model trained on one task is adapted to perform a different but related task, reducing the data and compute needed.

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