Plain-English Explainer
What Is Constitutional AI?
A written set of principles, baked into the training process itself.
Constitutional AI is the training approach Anthropic developed to make Claude helpful and safe without relying entirely on human feedback for every judgment call. This page explains what the method is, how it differs from standard reinforcement learning from human feedback, and why the distinction matters for anyone trying to understand how large language models are aligned.
Constitutional AI is Anthropic's training method where the model critiques and revises its own outputs against a written set of explicit principles. Instead of requiring humans to label every harmful response, the model learns to evaluate itself using the constitution as a reference. The result is a training signal that scales and is more transparent about the values being instilled.
Core Concepts
The three moving parts of Constitutional AI
The method combines a written constitution, AI self-critique, and revised reinforcement learning into one pipeline.
The constitution
A document of explicit principles guides the model during training. These cover helpfulness, honesty, and harm avoidance, and they are readable by anyone, not buried inside a dataset.
AI feedback (RLAIF)
Rather than asking humans to label every output, the model is prompted to evaluate its own responses against the constitution. This is called reinforcement learning from AI feedback.
Critique and revision
The model generates a response, critiques it by checking it against specific principles, then rewrites it. This loop runs during supervised fine-tuning before the reinforcement learning stage.
Transparency
Because the guiding values are written down, researchers and the public can read and debate them. That is a significant shift from alignment methods where the values are implicit in a human-labeled dataset.
How It Works
From draft response to revised output
The Constitutional AI pipeline has two main phases. In the first phase, supervised learning, the model is shown a prompt and generates an initial response. It is then given a principle from the constitution and asked whether the response violates it. If it does, the model rewrites the response. These revised responses become the training data for the next step.
In the second phase, reinforcement learning from AI feedback, a separate model is trained to score responses based on how well they follow the constitution. That scoring model then provides the reward signal for further training. The human feedback that remains is focused on helpfulness rather than on labeling harm, which is where scaling gets expensive and inconsistent.
The practical result is a model that has internalized a specific, stated set of values rather than a model that has learned to imitate whatever the median human rater preferred on a given day.
Comparison
How this differs from standard RLHF
Standard reinforcement learning from human feedback (RLHF) asks human raters to compare pairs of model outputs and indicate which is better. The model learns to generate outputs that raters prefer. This works well, but it has limits: it is expensive to scale, rater preferences vary, and the values being learned are never written down in one place.
Constitutional AI keeps human judgment in the loop for helpfulness comparisons but replaces most of the harm-avoidance labeling with the written constitution and AI critique. This makes the value alignment more explicit and the training process cheaper to scale for the safety-relevant portion of the work.
Neither approach is a complete solution on its own. Constitutional AI still depends on the quality of the principles in the constitution, and the constitution itself reflects choices made by Anthropic researchers. The method is more transparent than pure RLHF, but transparency about values is not the same as having the right values.
Why It Matters
What Constitutional AI changes in practice
Auditable values
The principles are a public document. Researchers, journalists, and users can read what the model was trained to prioritize, which is not possible with an opaque human-labeled dataset.
Scalable safety feedback
Using the model to critique itself reduces the volume of human labeling needed for harm avoidance, making it more practical to train larger models with consistent safety behavior.
Separation of concerns
Helpfulness and safety training are partially decoupled. Human raters focus on what counts as a good answer; the constitution handles what counts as a safe one.
Learn to work with Claude, not just read about it
Understanding how Claude was trained changes how you prompt it. The free Claude Academy curriculum starts with hands-on exercises, not theory. You will learn what Claude responds to and why from the first lesson.
Frequently Asked Questions
Who invented Constitutional AI?
Constitutional AI was developed by researchers at Anthropic and introduced in a 2022 paper. It was created as an alternative to relying solely on human feedback for teaching models to avoid harmful outputs.
What is the constitution in Constitutional AI?
The constitution is a written document containing explicit principles about how the model should behave. It covers areas like honesty, avoiding harm, and respecting autonomy. During training, the model checks its own outputs against these principles and rewrites responses that violate them. The principles are not hidden inside a dataset; they are readable text.
Is Constitutional AI the same as RLHF?
No. RLHF (reinforcement learning from human feedback) uses human raters to score model outputs. Constitutional AI replaces much of that human scoring for safety-related judgments with AI self-critique guided by written principles. Anthropic's training still uses human feedback for helpfulness, so the two approaches are combined rather than one replacing the other entirely.
Does Claude always follow its constitution?
Claude is trained to follow the principles, but no training method produces perfect adherence in all situations. The constitution shapes Claude's dispositions during training; it does not function as a hard rule-checker at inference time. Edge cases, novel situations, and adversarial prompts can still produce outputs that deviate from the intended principles.
Can I read Anthropic's constitution?
Yes. Anthropic has published the model spec and the principles used in Constitutional AI training. The published documents describe the values and priorities the model is trained to uphold. Searching for 'Anthropic model spec' will find the current version. This transparency is one of the stated goals of the Constitutional AI approach.
Why does Constitutional AI matter for AI safety?
Constitutional AI matters because it makes the values embedded in a model explicit and debatable rather than implicit in a dataset. When values are written down, it is possible to argue about whether they are correct, update them, and audit whether the model follows them. That is harder to do when alignment comes purely from human rater preferences aggregated across millions of comparisons.
Does Constitutional AI make Claude perfectly safe?
No training method produces a perfectly safe model, and Anthropic does not claim Constitutional AI does. The method improves consistency and transparency in safety-relevant training, and it scales better than labeling every harmful output by hand. But the quality of the outcome still depends on the quality of the principles written into the constitution and on the judgment calls made by the researchers who wrote them.
How does Constitutional AI affect how I should prompt Claude?
Knowing that Claude was trained against explicit principles helps you understand its refusals. When Claude declines a request, it is usually because the request conflicts with a specific principle around harm, honesty, or autonomy rather than a vague sense of discomfort. Framing requests clearly and in good faith tends to work better than trying to find workarounds, because the training was designed to recognize intent, not just surface wording.
Put this into practice
Understanding the training is the first step. Using Claude well is the skill.