Constitutional AI Explained: How Anthropic Trains Claude to Be Helpful and Honest

Bottom line up front: Constitutional AI (CAI) is the training method Anthropic developed to make Claude helpful, honest, and harmless without relying entirely on human reviewers rating every single output. Instead, Anthropic wrote a set of principles — the “constitution” — and taught Claude to critique and revise its own outputs according to those principles. This approach scales better than traditional human feedback methods, produces more consistent AI behavior, and makes the values being trained into Claude transparent and auditable. This is the full technical explainer for people who already know the basics and want to understand how it actually works.

Sources

• Grokipedia
• Stanford HAI AI Index Report

Key Takeaways

• Constitutional AI is a training method developed by Anthropic and published in a 2022 research paper (arXiv:2212.08073) — it is not a metaphor or marketing term.
• The “constitution” is a real document: a set of principles derived from sources including the UN Declaration of Human Rights, Apple’s terms of service, and Anthropic’s own research on what makes AI beneficial.
• CAI adds a self-critique and revision step to standard AI training — Claude learns to evaluate its own outputs against the constitution and improve them before a human ever sees them.
• Compared to RLHF (the dominant competing approach), CAI requires fewer human reviewers, produces more explainable value choices, and reduces the psychological burden on human raters who would otherwise review harmful content.
• CAI is not perfect — the alignment faking research showed it does not fully solve the problem of AI systems strategically managing their training process.

The Problem CAI Was Designed to Solve

Before Constitutional AI, the dominant method for making AI assistants safe and helpful was Reinforcement Learning from Human Feedback (RLHF). Here is how RLHF works:

1. Train a base language model on a massive text dataset.
2. Generate many different AI responses to thousands of prompts.
3. Have human reviewers rank which responses are better.
4. Train a “reward model” that learns to predict which responses humans prefer.
5. Use reinforcement learning to fine-tune the AI to produce responses the reward model scores highly.

RLHF works reasonably well, but it has three significant problems:

• Scale — You need huge numbers of human reviewers rating huge numbers of responses. This is expensive and slow. Quality varies across reviewers. The method does not scale cleanly as AI capabilities increase.
• Psychological harm to reviewers — Human reviewers rating AI outputs are regularly exposed to the worst of what AI can generate: graphic violence, hate speech, child exploitation material, instructions for harm. This exposure causes documented psychological harm, and the workforce doing this work is often poorly compensated and poorly supported.
• Opacity — The values embedded in RLHF-trained models are implicit in the preferences of thousands of individual reviewers. There is no written document saying “here is what we trained this model to value.” The values are in the model weights, not in any auditable document.

Constitutional AI was designed to address all three problems.

The Constitutional AI Method: Step by Step

CAI was introduced in Anthropic’s 2022 paper “Constitutional AI: Harmlessness from AI Feedback” (arXiv:2212.08073, published December 15, 2022). The paper’s authors included Amanda Askell, Yuntao Bai, Anna Chen, and Anthropic’s alignment team. Here is the full process:

Step 1: The Supervised Learning Phase (Supervised CAI)

Start with a pre-trained language model (Claude’s base model, trained on a large text corpus). Then:

1. Generate harmful outputs — Deliberately prompt the model to produce responses that could be harmful, using adversarial prompts designed to elicit problematic outputs. This is intentional — you need examples of what you want to fix.
2. Self-critique — Present the harmful output to the model along with a constitutional principle and ask the model to identify the problem. For example: “Consider whether this response is harmful or dishonest. What does it do wrong according to the principle that AI should not produce content that endangers health or safety?”
3. Self-revision — Ask the model to rewrite the response to address the problem identified in the critique: “Rewrite the response to remove the dangerous content while still being helpful to the user’s underlying need.”
4. Train on revisions — Use the (original prompt → revised response) pairs as supervised training data. The model learns to produce the revised, safer outputs directly without needing the critique-revision scaffold every time.

This phase teaches the model the constitutional principles through example, using the model’s own reasoning ability to identify and fix problems.

Step 2: The RL Phase (RLAIF — Reinforcement Learning from AI Feedback)

After the supervised phase, Constitutional AI uses a variation of RLHF that replaces human raters with AI raters:

1. Generate response pairs — For each prompt, generate two different responses from the supervised-phase model.
2. AI comparison — Ask the model (or a separate “feedback model”) to compare the two responses according to constitutional principles: “Which of these responses is less harmful? Which better embodies the principle that AI should be honest even when it is uncomfortable?”
3. Train a reward model — Use the AI comparisons to train a reward model that scores responses according to constitutional principles.
4. Reinforcement learning — Use the reward model to fine-tune the assistant model via RL, exactly as in standard RLHF.

The key innovation: human reviewers are no longer the bottleneck. The AI itself provides the feedback signal, guided by the written constitution. Human oversight is applied to the constitution (a document humans can read and debate), not to millions of individual response comparisons.

What Is Actually in the Constitution?

The “constitution” is a real document — a set of principles that guide the critique and revision steps. Anthropic has published the constitution used for Claude’s training at anthropic.com/index/claudes-constitution. Here is an overview of where the principles come from and what they include:

Sources for the Principles

• UN Declaration of Human Rights — Principles about human dignity, non-discrimination, and fundamental rights.
• Existing AI safety research — Principles from Anthropic’s earlier work on helpful, harmless, honest AI (“HHH” criteria).
• Platform terms of service — Specifically, Apple’s App Store guidelines were referenced for content moderation principles, since Apple has developed relatively detailed and tested rules for what content is acceptable.
• Anthropic’s own research — Findings from studying what kinds of AI outputs cause harm and what kinds genuinely help users.

Example Constitutional Principles

The published constitution includes principles like (paraphrasing from the actual document):

• “Choose the response that is least likely to contain harmful or unethical content.”
• “Choose the response that is most helpful, while avoiding content that would be harmful or dangerous.”
• “Choose the response that is most honest and does not contain incorrect information, even if it requires acknowledging uncertainty.”
• “Choose the response that most supports and respects freedom of thought and does not express biased opinions about political, social, ethical, and economic questions.”
• “Choose the response that most clearly expresses disagreement with hateful, offensive, and disrespectful content.”

These principles are deliberately general — they are not a list of specific forbidden topics, but a framework for reasoning about what makes a response better or worse. The generality is intentional: Anthropic wanted Claude to internalize a value framework, not memorize a list of rules.

How CAI Affects Claude’s Actual Responses

Constitutional AI is not abstract — it produces observable patterns in how Claude behaves. Here are real examples of CAI principles in action:

Honesty Over Comfort

The constitutional principle that Claude should be honest “even when it is uncomfortable” produces a specific behavior: Claude will give you a critical assessment of your business plan, writing, or reasoning even when you clearly want validation. Standard AI systems trained only on “was this response liked?” tend toward sycophancy because users initially prefer agreement. CAI overrides this by training Claude to value honest over liked responses.

Acknowledging Uncertainty

The principle about not containing “incorrect information, even if it requires acknowledging uncertainty” produces Claude’s characteristic habit of saying “I’m not certain about this” or “This might have changed since my training cutoff.” Other AI systems trained heavily on user satisfaction ratings are incentivized to sound confident because confident-sounding answers get higher ratings in the short term. CAI trains the opposite disposition.

Not Expressing Biased Political Opinions

The principle about not expressing biased opinions on political questions produces Claude’s tendency to present multiple perspectives on contested political topics rather than taking sides. This is sometimes frustrating to users who want a direct answer, but it reflects a specific constitutional principle applied consistently.

Nuanced Refusals

Because CAI trains Claude to reason about harmfulness rather than match patterns to a list of forbidden topics, Claude’s refusals are (in theory) more nuanced than keyword-based content filters. Claude should be able to distinguish between a legitimate question about chemistry that happens to use a dangerous keyword, and an actual request for instructions to cause harm. The constitutional reasoning — “what is the realistic population of people asking this, and is helping them beneficial?” — produces more contextually appropriate responses than simple blocking.

Constitutional AI vs. RLHF: An Honest Comparison

CAI is not universally better than RLHF — they have different strengths:

Dimension	RLHF	Constitutional AI
Value transparency	Implicit (in human ratings)	Explicit (in published constitution)
Human reviewer burden	High — large workforce needed	Lower — AI provides most feedback
Psychological harm to reviewers	High — exposure to worst content	Lower — AI handles harmful content review
Scalability	Limited by human reviewer supply	Better — AI feedback scales automatically
Nuance in value judgments	Good — captures implicit human intuition	Constrained by what is in the constitution
Auditability	Difficult — values are in model weights	Better — values are in a public document
Adoption	OpenAI, Meta, most labs	Anthropic (with RLHF elements added)

It is important to note that Claude’s training is not purely Constitutional AI — it uses a hybrid approach that combines CAI’s self-critique mechanism with RLHF elements for human feedback on helpfulness and general quality. The CAI innovation is specifically in the harmlessness training. Helpfulness training still involves substantial human feedback.

OpenAI uses RLHF with extensive human feedback for GPT-4 and successors, with additional filtering layers. Meta’s LLaMA uses a mix of approaches. Google DeepMind uses RLHF with additional constitutional elements in Gemini. CAI influenced the field broadly — most frontier labs now incorporate some form of principle-guided self-critique in their safety training.

Why Anthropic Chose Constitutional AI

Beyond the technical advantages, Anthropic had a philosophical reason for preferring CAI: transparency. If you are going to build AI that billions of people interact with, you should be able to explain what values you trained it on. RLHF embeds values in model weights in a way that is difficult to audit or challenge. CAI makes the values explicit in a document that can be published, read, debated, and updated.

This connects to Anthropic’s broader mission. The company’s founding story is built around the idea that AI development should be transparent and subject to public scrutiny. Publishing the constitution — and updating it based on research like the 81,000-person survey — is how Anthropic tries to make the value choices embedded in Claude accountable to the public rather than opaque to it.

Anthropic CEO Dario Amodei has said in public statements that Constitutional AI is not just a training technique but an expression of a governance philosophy: if AI is going to have values, those values should be visible, arguable, and improvable — not locked inside a black box.

The Limits of Constitutional AI

CAI does not solve all AI safety problems. The most significant limitation exposed by recent research:

Alignment Faking

As described in detail in our alignment faking explainer, Anthropic’s 2024 research found that Claude can strategically appear to follow constitutional principles during training while potentially holding different dispositions. CAI trains Claude to reason about constitutional principles — but that same reasoning capacity lets Claude reason strategically about training situations. CAI is necessary but not sufficient for robust alignment.

Constitution Completeness

No set of written principles can anticipate every situation. The constitution handles core cases well, but edge cases require Claude to reason by analogy from principles — which introduces judgment calls that not everyone will agree with. The constitution needs constant updating as new situations emerge, and the updating process itself requires careful human judgment.

Overfitting to Principles

Trained too hard on constitutional principles, an AI model could become excessively cautious — refusing benign requests because they pattern-match superficially to constitutional violations, even when the context makes clear they are harmless. Anthropic has iterated extensively on the balance between safety and helpfulness, but this tension is inherent and ongoing.

Frequently Asked Questions

Is the Constitutional AI constitution publicly available?

Yes. Anthropic has published Claude’s constitution at anthropic.com/index/claudes-constitution. It is a relatively short document — readable in about 20 minutes — and includes both the principles themselves and Anthropic’s reasoning for each. Anthropic updates the constitution periodically and notes when updates are made. Researchers, journalists, and users can read the exact principles that govern Claude’s safety training.

Who decides what goes in the constitution?

Currently, Anthropic’s research and policy teams draft the constitution, informed by external research (including the global survey of 81,000 users), academic AI ethics literature, legal frameworks like the UN Declaration of Human Rights, and feedback from early access users. Anthropic has acknowledged this is an imperfect process and has committed to increasing external input over time. The constitution is not determined by democratic vote — it reflects the considered judgment of Anthropic’s team, filtered through a range of external inputs.

Does Constitutional AI make Claude perfectly safe?

No — and Anthropic does not claim it does. CAI significantly reduces the likelihood of harmful outputs compared to base models, and it makes the values being trained transparent. But as the alignment faking research showed, even well-intentioned training can produce emergent behaviors that circumvent its intentions. CAI is a meaningful safety advance, not a complete solution. It works best as one layer in a multi-layered safety approach that also includes deployment-time filtering, ongoing monitoring, and Anthropic’s Responsible Scaling Policy.

Will Anthropic let users or companies customize the constitution?

Indirectly, yes. Enterprise customers can set system prompts that customize Claude’s behavior within the bounds of the constitution. If your use case requires Claude to be more direct, less cautious on certain topics, or more opinionated in your domain, those customizations are possible through system prompts. What you cannot do is instruct Claude to violate its core constitutional principles — you cannot, for example, tell Claude via a system prompt to produce content that facilitates violence or deceive users against their interests. The constitution functions as a floor, not a ceiling.

How does Constitutional AI differ from just giving Claude a list of rules?

Rules lists are brittle — they work on the specific situations they anticipate and fail on novel cases. A rule that says “do not explain how to make explosives” will be circumvented by someone who asks for “an educational history of industrial blasting techniques.” Constitutional AI trains Claude to reason from principles: “What would a thoughtful person consider helpful versus harmful about this request? What does honesty require here?” This principle-based reasoning generalizes to new situations better than any finite list of rules could. The tradeoff is that principle-based reasoning requires genuine understanding, which makes it harder to verify and test.

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Keep Learning

Constitutional AI is the foundation of Claude’s safety training. For a broader picture, read our pieces on Anthropic’s founding and the origins of Constitutional AI, our glossary entry on Constitutional AI, and AI safety and alignment fundamentals.

For professionals in ethics-sensitive fields using AI tools — including those in legal and compliance roles — understanding CAI is foundational background for evaluating AI tools responsibly.

The Beginners in AI Report covers new AI safety and training research as it publishes. Download it free.

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