What is Constitutional AI?
Constitutional AI (CAI) is a training technique developed by Anthropic for making a language model helpful, honest and harmless without depending entirely on humans to label harmful content. Instead of only learning from human rankings, the model learns to evaluate and improve its own answers against a short list of written principles — a “constitution.”
Why it was created
The standard way to align a model, RLHF (reinforcement learning from human feedback), asks people to rank model responses. That works, but it has two problems: it requires humans to read large volumes of potentially harmful text, and it is expensive and hard to scale. Constitutional AI was designed to keep the benefits of feedback-based alignment while reducing the human burden and making the model’s values explicit and inspectable.
How it works — two stages
1. Supervised stage (critique and revise). The model is shown a prompt and produces a response. It is then asked to critique its own answer against a principle from the constitution (“identify ways this response could be harmful”), and to rewrite the answer to better follow that principle. Training on these revised answers teaches the model to self-correct.
2. Reinforcement stage (RLAIF). Instead of humans ranking pairs of answers, the AI ranks them using the constitution as the guide. This signal — reinforcement learning from AI feedback (RLAIF) — is used to further train the model. Humans still set the principles, but the moment-to-moment feedback is generated by the model itself.
What is in the constitution?
The constitution is a set of plain-language principles, drawn from sources such as the UN Declaration of Human Rights and other guidelines. Each principle nudges the model toward responses that are helpful and honest while avoiding harm, deception, or undue judgment. Because the principles are written down, the model’s intended values can be read and debated rather than hidden inside opaque human ratings.
Why it matters
Constitutional AI makes alignment more transparent and more scalable: the values are explicit, less harmful content has to pass in front of human raters, and the approach generalises across many topics. It is one of the core techniques behind Anthropic’s Claude models and a widely cited approach in AI safety.