Constitutional AI

What is Constitutional AI?

Constitutional AI (CAI) is an alignment technique where a language model critiques and revises its own outputs according to a set of written principles, reducing reliance on human feedback for safety training. The model acts as its own reviewer, identifying violations of its constitution and generating improved responses.

The process has two phases. In the supervised phase, the model generates responses to potentially harmful prompts, then critiques those responses against constitutional principles (e.g., "Choose the response that is least harmful") and produces revised outputs. The revised responses become training data. In the reinforcement learning phase, the model's own preference judgments — guided by the constitution — replace human preference labels for RLHF training.

This approach scales safety training beyond human annotation capacity. A human-written constitution of 10-20 principles can generate millions of training examples through self-critique. The principles are explicit and auditable, making the safety behavior more transparent than systems trained purely on implicit human preferences. Anthropic introduced CAI and uses it as a core component of Claude's training.

Why does Constitutional AI matter?

Constitutional AI makes safety training more scalable, transparent, and controllable than pure RLHF. Written principles can be audited, debated, and updated without retraining a reward model. This transparency is increasingly important as AI systems are deployed in regulated industries that require explainable safety mechanisms.

How is Constitutional AI used in practice?

Anthropic applies constitutional AI to train Claude models with principles covering helpfulness, harmlessness, and honesty. Organizations building on top of foundation models can implement lightweight constitutional approaches by having a reviewer model critique outputs against company-specific policies before serving them to users.