Constitutional AI

Constitutional AI is an alignment technique, developed by Anthropic, in which a model is trained to critique and revise its own responses against a written set of principles — a "constitution" — instead of relying purely on humans to label every harmful or unhelpful output. The model generates an answer, checks it against the principles, rewrites it, and those self-revisions become training signal; a later stage uses AI-generated preference feedback (RLAIF) rather than human raters for much of the safety tuning. Anthropic's Claude models are trained this way. For builders, the concept is a useful mental model for how modern assistants get their behavior and boundaries: the rules a model follows can be made explicit and inspectable rather than buried in millions of human labels. Practical note: you can't edit a hosted model's constitution, but you can shape behavior at your own layer with a clear system prompt that states your principles and priorities — think of it as a lightweight constitution for your application. Two misreadings are worth heading off. The constitution is not handed to the model as a runtime system prompt during normal use — it is primarily a training-time technique that shapes learned behaviour, not a document the model consults at inference, so you cannot inspect or override it from an API call the way you can your own instructions. And it does not guarantee flawless adherence to every stated principle. Like every alignment method, it reduces undesired behaviour without eliminating it; a model trained this way still refuses inconsistently at the edges and can still be pushed off-policy by a determined jailbreak. What the approach genuinely buys is scale and auditability. Because much of the preference labelling is generated by an AI following written principles rather than by human raters — the family of techniques known as RLAIF — the labelling bottleneck that constrains purely human feedback largely disappears, cutting both the cost and the elapsed time of safety training. And because the principles are written down, they can be read, argued with, and revised, which is a meaningfully different accountability story from millions of opaque human preference judgements that cannot be inspected after the fact. The obvious dependency is that quality now rests on the guiding model's own judgement and on how clearly the constitution is specified: a vague or badly chosen principle propagates at scale rather than being averaged out by many independent raters. Human oversight does not disappear either — people still write the constitution and audit the outcomes.