What is Constitutional AI?

Constitutional AI addresses limitations of pure RLHF by encoding desired behaviors as a set of written principles that the AI system uses to evaluate and revise its own outputs. The approach was introduced by Anthropic in 2022 and represents an important evolution in alignment methodology.

The CAI training process involves two phases. In the first phase, a language model generates responses to prompts, then critiques and revises its own responses according to the constitutional principles. This self-supervised revision process produces a dataset of improved responses. In the second phase, a preference model is trained on the revised outputs and used for reinforcement learning, similar to RLHF but with the critical difference that preferences are partially derived from principles rather than entirely from human labelers.

The constitution itself is a set of natural language statements describing desired model behavior, such as avoiding harmful content, being truthful, and respecting user autonomy. By making these principles explicit and auditable, CAI provides greater transparency compared to approaches where alignment criteria are implicit in human preference data. The principles can be updated and debated publicly, enabling broader participation in defining AI behavior standards.

CAI offers several advantages over pure RLHF. It reduces the volume of human feedback needed, scales more easily to cover diverse scenarios, and makes alignment criteria transparent and modifiable. It also reduces the risk of encoding individual labeler biases by grounding behavior in explicit principles rather than subjective preferences. The approach has been influential in the broader field of AI alignment and has been adopted or adapted by other organizations.