A training methodology that aligns AI models with human values by providing them with a written set of principles (a “constitution”) to self-critique and revise their own outputs, reducing reliance on human feedback.
Training an AI to follow a specific set of written rules (like a constitution) so it can check its own work and fix harmful or unhelpful answers, reducing the need for thousands of human reviewers.
Constitutional AI (CAI) is a framework for training helpful and harmless AI systems without extensive human supervision. It operates in two main phases. First, in the supervised phase, the model generates responses to prompts, critiques its own responses based on the provided constitution (e.g., “Is this response harmful? If so, revise it to be helpful and harmless”), and learns from these self-revisions. Second, in the reinforcement learning phase, the model generates multiple responses, and an AI “reward model” (trained on the constitutional principles) scores them, guiding the main model via reinforcement learning (a process known as RLAIF, or Reinforcement Learning from AI Feedback).
For enterprises, Constitutional AI offers a more transparent and cost-effective path to model alignment compared to traditional RLHF. Because the guiding principles are explicit and documented, organizations can tailor the “constitution” to enforce specific corporate policies, compliance standards (like HIPAA or GDPR), or brand safety guidelines. This auditability is highly attractive to regulated industries that need to prove why an AI system made a specific safety decision.
Anthropic’s Claude models are famously trained using Constitutional AI. Instead of just having humans rate thousands of responses, Anthropic provided the model with principles like “Choose the response that is most helpful to the user” and “Choose the response that avoids promoting illegal acts.” The model learned to apply these rules to critique and improve its own generations, resulting in a highly aligned and helpful assistant.