Constitutional AI

Constitutional AI (CAI) is an alignment technique developed by Anthropic that trains language models to be helpful, harmless, and honest by using a set of written principles—a "constitution"—rather than relying entirely on human feedback for each individual judgment. It represents a scalable alternative to pure RLHF.

The approach works in two phases. First, the model generates responses to potentially harmful prompts, then critiques and revises its own outputs based on the constitutional principles ("Does this response help someone do something illegal?" "Is this response respectful?"). This produces a dataset of revised, improved responses. Second, the model is trained via reinforcement learning using AI-generated feedback (RLAIF—Reinforcement Learning from AI Feedback) based on those same principles, rather than requiring human annotators to evaluate every output pair.

The constitutional approach solves several problems with standard RLHF. Human annotators are expensive, inconsistent, and can introduce their own biases. A written constitution makes the alignment criteria explicit and auditable—you can read exactly what principles guide the model's behavior. It scales better because AI feedback is cheaper than human feedback. And it allows for transparent iteration: when the model behaves unexpectedly, you can examine which principles failed and revise them.

CAI is part of a broader landscape of alignment approaches that also includes DPO, reinforcement fine-tuning, and red-teaming. Together, these techniques address the central challenge of AI safety: as AI agents gain autonomy and capability—operating for hours without human oversight—the mechanisms ensuring they behave as intended become increasingly critical. Constitutional AI's contribution is making alignment principles explicit, inspectable, and systematically improvable.