Constitutional AI (CAI) is a method for training large language models to be helpful and harmless in which the model critiques and revises its own outputs according to a written set of principles, called a constitution, rather than relying primarily on human feedback for every example of undesirable behavior. It was introduced by Anthropic in a December 2022 paper, "Constitutional AI: Harmlessness from AI Feedback," and became a core training method behind the Claude (AI model family) model family.
Method
The technique proceeds in two stages. In the supervised stage, the model generates a response to a prompt, critiques its own response against the constitution's principles, and rewrites the response to better satisfy them; this self-critique-and-revise loop produces a dataset used for supervised fine-tuning. In the reinforcement learning stage, the model compares pairs of its own responses and judges which one better follows the constitution, generating a preference dataset that trains a reward model, a process Anthropic termed reinforcement learning from AI feedback, or RLAIF. That reward model is then used to fine-tune the model with reinforcement learning, a structure that parallels RLHF but replaces many human-provided preference labels with AI-generated ones.
Motivation
Anthropic researchers, including cofounder Jared Kaplan, argued that pure RLHF has two weaknesses: it requires large volumes of human labor to review harmful outputs, work that is slow and can expose labelers to distressing content, and it encodes only implicit, unwritten judgment calls rather than an auditable written standard. A constitution, by contrast, can be inspected, debated, and revised independently of any individual labeler's preferences, and the same document can be reused across training runs. Dario Amodei, Anthropic's chief executive, has framed the approach as part of a broader bet that scaling capable models safely requires methods that do not depend on ever-larger amounts of human review.
The constitution
The principles in Anthropic's original constitution drew on multiple sources, including the United Nations Universal Declaration of Human Rights, trust-and-safety norms from other technology companies, and instructions meant to keep the model non-evasive and attentive to perspectives outside a narrow Western default. Anthropic has published successive revisions of Claude's constitution and describes it as an evolving document rather than a fixed specification.
Relationship to alignment and safety
Constitutional AI is one of several techniques researchers use to pursue AI alignment between a model's behavior and its designers' intentions, alongside plain RLHF and Direct Preference Optimization. Proponents argue it improves scalability, since AI-generated feedback can be produced far faster than human feedback, and improves transparency, since the constitution itself can be read and criticized by outsiders. It is also presented as a partial answer to Reward hacking, on the reasoning that grounding preferences in explicit written principles makes it harder for a model to satisfy a reward signal while violating the spirit of the underlying goal.
Criticism
Critics have raised several objections. Because the model used to critique and revise responses draws on the same broad capabilities as the model being trained, some researchers question whether AI feedback can catch failures that a comparably capable human overseer would miss, particularly as models approach or exceed human performance on the tasks being judged. Others note that the choice of which principles to include, and how to resolve conflicts between them, remains a decision made by a single company, so the method does not by itself settle disputes about whose values an AI system should reflect. Constitutional AI also does not remove the need for Red teaming (AI) and other guardrail mechanisms, since a written principle can still be circumvented by adversarial prompting such as a Jailbreak (AI).
Legacy
The self-critique pattern behind Constitutional AI influenced later research on using models to supervise or evaluate other models, sometimes called LLM-as-judge evaluation, and it remains one of the more widely cited examples of a frontier lab publishing detailed alignment methodology rather than keeping it as a trade secret.