Constitutional AI

Definition

A training methodology developed by Anthropic where an AI model evaluates its own outputs against a written set of principles (a 'constitution') and self-corrects, reducing reliance on human feedback for safety alignment.

Why it matters

Constitutional AI addresses a fundamental scaling problem in alignment: human feedback is expensive, slow, and inconsistent. By teaching models to self-evaluate against explicit principles, you can scale safety alignment without proportionally scaling human oversight. This approach also makes alignment more transparent, as the constitution is a readable document that stakeholders can review and debate. For enterprise buyers, Constitutional AI means the model's safety behavior is principled and auditable, not a black box of RLHF data. The trade-off is that constitutional training can make models overly cautious, refusing legitimate requests because they trigger principle violations.

In practice

Anthropic published the Constitutional AI paper in December 2022 and has used the approach as a core component of Claude's training. The constitution includes principles like helpfulness, honesty, and harm avoidance, which the model uses to critique and revise its own outputs during training. This reduces the need for human raters by 50-80% compared to pure RLHF. Other labs have adopted similar self-evaluation approaches: Google's RLAIF (Reinforcement Learning from AI Feedback) and Meta's self-reward training both draw on the insight that models can provide useful training signal about their own outputs.