AI_SLANG_ENTRY
Constitutional AI Meaning
Constitutional AI is a training method that uses a written set of principles (a constitution) to have one AI model critique and revise another model's outputs, then applies reinforcement learning from AI feedback rather than from human feedback alone.
What does Constitutional AI mean?
Constitutional AI is a training method that uses a written set of principles (a constitution) to have one AI model critique and revise another model's outputs, then applies reinforcement learning from AI feedback rather than from human feedback alone.
The model is given a list of principles, then asked to rewrite its own draft so the draft follows the principles. Once enough of that self-revision has happened, the team trains on AI-generated feedback rankings so the rule-following behavior sticks.
Origin and usage
Introduced in Anthropic's 2022 Constitutional AI paper. The framing aimed to scale helpful-harmless training beyond costly human-labelled preference data by letting the model judge its own drafts against explicit principles, then learning from AI-generated preference rankings.
Source type: paper. Last checked: 2026-07-25.
Originated as an Anthropic research project. The 2022 Constitutional AI paper (Bai et al.) is the canonical reference; Anthropic's later public material discusses the approach as part of how Claude is trained.
How constitutional AI works
- A constitution is written: a list of principles the model is supposed to follow.
- The model is asked to critique its own draft against the constitution and produce a revised response.
- The critiques and revisions are used to create AI-generated preference data.
- Reinforcement learning from AI feedback (RLAIF) trains the model on those preferences.
Constitutional AI versus RLHF
Classical RLHF depends on humans writing preference labels for many model outputs. Constitutional AI swaps part of that human-labelling step for AI-generated feedback guided by a written principles list.
The trade is scale versus trust in the principles: AI feedback is cheaper to produce, but the principles themselves are still chosen by people, and the model can only critique what it is able to articulate.
What constitutional AI is not
- It is not a single universal constitution; Anthropic's constitution is one example of how such a principles list can be written.
- It is not a substitute for red teaming, safety evaluation, or content policy review.
- It is not the same as asking for a system prompt at inference time; the constitution shapes training behavior, not just one conversation.
- It does not by itself guarantee value alignment, only that the model is more consistent with the listed principles during training.
Examples
- The lab trained its assistant with constitutional AI so the model could revise harmful responses using a written ruleset.
- Constitutional AI does not remove the need for red teaming; it changes what the red team is checking.
FAQ
What is Constitutional AI?
Constitutional AI is a training method introduced by Anthropic in which a model critiques and revises its own outputs against a written set of principles, then is fine-tuned with reinforcement learning from AI feedback.
How is Constitutional AI different from RLHF?
Both shape model behavior from preference data, but RLHF uses human-written preference labels, while Constitutional AI uses AI-generated preferences guided by a written principles list.
Does Constitutional AI remove the need for human feedback?
No. It reduces the volume of preference labels humans must write, but people still choose the constitution, the training pipeline, and the safety evaluations.
Is Constitutional AI only used by Anthropic?
The original paper is from Anthropic, but the broader idea of model self-critique guided by explicit rules has influenced other alignment research and product systems.