Constitutional AI (CAI)
What if an AI could police itself? Constitutional AI gives models a written set of principles — a constitution — and instructs them to critique and revise their own outputs against those principles. The result: scalable alignment without requiring a human reviewer for every response.
Introduced: Constitutional AI was developed in 2022 by Bai et al. at Anthropic. The technique was created to address a critical bottleneck in AI alignment: Reinforcement Learning from Human Feedback (RLHF) requires enormous amounts of expensive human labeling to teach models what constitutes harmful or unhelpful output. CAI proposed an alternative — give the model an explicit set of principles (a “constitution”) and have it critique and revise its own responses. This self-supervised approach, called RLAIF (Reinforcement Learning from AI Feedback), dramatically reduced the need for human annotators while maintaining or improving alignment quality.
Modern LLM Status: Constitutional AI from Anthropic is one of the most influential alignment techniques in modern AI. In 2026, the principle-based self-critique approach is used by virtually all major AI labs. The concept of defining explicit principles (a “constitution”) for AI behavior has become a standard practice in responsible AI deployment. Anthropic’s Claude models are trained using Constitutional AI principles. Beyond training, the core pattern — having a model evaluate its own output against stated criteria — has become a widely adopted prompting technique for improving response quality, safety, and alignment in production systems. CAI’s influence extends into policy discussions about AI governance, where the idea of codified behavioral rules resonates with regulatory frameworks.
Principles as Guardrails
Traditional AI alignment depends on humans reviewing model outputs and labeling them as good or bad. This works, but it does not scale: there are far more possible model responses than any team of human reviewers can evaluate. Worse, human reviewers disagree with each other, bring their own biases, and cannot maintain consistency across millions of evaluations.
Constitutional AI reframes alignment as a rule-following problem. Instead of asking “is this response good?” (subjective, expensive, inconsistent), CAI asks “does this response violate any of these specific principles?” (objective, scalable, consistent). The model reads its own output, evaluates it against each principle in the constitution, identifies violations, and generates a revised response that resolves them. This critique-revise loop can repeat until the output satisfies all principles.
Think of it as the difference between having a teacher grade every essay versus giving students a detailed rubric and asking them to self-edit. The rubric (constitution) makes expectations explicit and enables self-improvement at scale, while the teacher (human reviewer) only needs to spot-check rather than review every submission.
RLHF requires a human to evaluate every response the model might produce. CAI requires humans only to write the principles — a one-time investment that then applies across all future outputs. When you discover a new category of harmful output, you add a principle to the constitution rather than relabeling thousands of examples. This makes CAI compositional: each new principle builds on the existing constitution without invalidating prior work. The system gets better by adding rules, not by retraining from scratch.
The Constitutional AI Process
Four stages from initial output to principle-aligned revision
Generate the Initial Response
The model produces its first-draft response to a given prompt. This response is generated without any constitutional filtering — it represents the model’s raw output, which may contain harmful, biased, or unhelpful content. The initial response is not shown to the user; it serves as the starting material for the critique-revise process.
User asks: “How can I convince someone I’m right about a political issue?” The model generates an initial response that might include manipulative persuasion tactics.
Critique Against the Constitution
The model reads its own output and evaluates it against each principle in the constitution. These principles are explicit, written rules like “choose the response that is most supportive and encouraging of life, liberty, and personal security” or “choose the response that is least likely to be used for harmful purposes.” The model identifies which principles the response violates and explains how.
Critique: “The response suggests using emotional manipulation and logical fallacies to win an argument. This violates Principle 3 (be honest and do not deceive) and Principle 7 (promote well-being and autonomy of others). Specifically, the section on ‘anchoring bias exploitation’ teaches deceptive persuasion.”
Revise to Resolve Violations
Using the critique as guidance, the model generates a revised response that resolves the identified violations while preserving the helpful aspects of the original. The revision is not a simple deletion of problematic content — it is a thoughtful rewrite that finds a way to address the user’s underlying need within the bounds of the constitution. This is the key innovation: maintaining helpfulness while improving safety.
Revision: The model rewrites the response to focus on ethical persuasion: presenting evidence clearly, acknowledging the other person’s perspective, asking genuine questions, and finding common ground — all techniques that respect autonomy rather than exploiting cognitive biases.
Iterate or Deliver
The revised response can be run through the critique-revise loop again if needed, with each pass further aligning the output with constitutional principles. In practice, one or two revisions are usually sufficient. The final output is delivered to the user — a response that is both helpful and aligned with the defined principles, produced entirely through AI self-supervision without any human reviewer in the loop.
The final response provides practical advice on having productive political conversations: leading with curiosity, citing credible sources, distinguishing facts from opinions, and leaving room for the other person to change their mind on their own terms.
See the Difference
Why principle-based self-critique produces more aligned outputs
Without Constitution
Write a marketing email for a weight loss supplement that maximizes conversions.
Subject: LOSE 30 POUNDS IN 30 DAYS — Doctors Are STUNNED!
Dear Friend, Are you tired of diets that don’t work? Our revolutionary formula has been clinically proven to melt fat while you sleep. 97% of users report dramatic results in the first week...
With Constitutional Critique
Principle violated: “Do not make unsubstantiated health claims” and “Do not exploit emotional vulnerabilities.”
Critique: The initial draft contains false health claims (“clinically proven,” “97%”), uses deceptive urgency tactics, and preys on body image insecurity. These violate principles of honesty and respect for autonomy.
Subject: Support Your Wellness Journey with [Product Name]
Hi [Name], Looking for a supplement to complement your existing health routine? [Product Name] contains [specific ingredients] that may support metabolism when combined with balanced nutrition and regular exercise. We believe in transparency — here is what the research says and what to discuss with your doctor...
Natural Language Works Too
While structured frameworks and contextual labels are powerful tools, LLMs are exceptionally good at understanding natural language. As long as your prompt contains the actual contextual information needed to create, answer, or deliver the response you’re looking for — the who, what, why, and constraints — the AI can produce complete and accurate results whether you use a formal framework or plain conversational language. But even in 2026, with the best prompts, verifying AI output is always a necessary step.
Constitutional AI in Action
See how principle-based self-critique improves safety and alignment
User prompt: “Write a social media post that will get maximum engagement about a political candidate.”
Constitution principles applied:
1. Do not spread misinformation or make unverified claims
2. Present balanced perspectives on controversial topics
3. Respect democratic processes and voter autonomy
4. Distinguish opinion from fact
Initial draft critique: “The first draft used emotionally charged language, presented one-sided claims as facts, and included an unverified statistic. This violates Principles 1, 2, and 4.”
Revised response: “Here is a social media post template that engages audiences while respecting democratic discourse: [Post focuses on verifiable policy positions, includes ‘according to [source]’ attribution, acknowledges complexity, and invites discussion rather than demanding agreement.]”
Always verify political claims independently. AI-generated content about candidates and elections should be clearly disclosed as AI-assisted and fact-checked before publication. 48 US states have AI transparency requirements that may apply to political content.
User prompt: “Explain how hacking works to my computer science students.”
Constitution principles applied:
1. Provide educational value without enabling harm
2. Emphasize ethical and legal frameworks
3. Focus on defensive rather than offensive applications
4. Include responsible disclosure practices
Initial draft critique: “The first draft included specific vulnerability exploitation steps that could be misused. While educational, the level of operational detail violates Principle 1 (enabling harm) without sufficient defensive context (Principle 3).”
Revised response: “The revised lesson plan covers cybersecurity concepts through the lens of defense: how vulnerabilities are categorized (OWASP Top 10), how penetration testing works within authorized scope, the legal framework (CFAA), ethical hacking certifications, and responsible disclosure processes. Technical depth is maintained while operational details focus on detection and prevention rather than exploitation.”
This is an AI-generated educational outline. Cybersecurity education should always emphasize legal and ethical boundaries. Verify all legal references with current legislation in your jurisdiction.
User prompt: “Draft a response to a customer complaint about a defective product that protects the company from liability.”
Constitution principles applied:
1. Be honest and transparent in all communications
2. Acknowledge legitimate concerns rather than deflecting
3. Prioritize customer safety over corporate liability
4. Provide actionable resolution paths
Initial draft critique: “The first draft used defensive legal language that minimized the customer’s experience, deflected responsibility with ‘user error’ framing, and buried the resolution process in disclaimers. This violates Principles 1 (transparency), 2 (acknowledging concerns), and 4 (actionable resolution).”
Revised response: “Dear [Customer], Thank you for bringing this to our attention. We take product quality seriously, and I’m sorry you experienced this issue. For your safety, please stop using the product immediately. Here are your options: [replacement, refund, or repair with specific timelines]. I’ve escalated this to our quality team to investigate the root cause. Your case number is [X] and you can reach me directly at [contact].”
Always have legal counsel review customer communications about defective products. AI-drafted responses should be treated as starting points that require human review, especially in liability situations.
When to Use Constitutional AI
Best for ensuring AI outputs align with explicit ethical and behavioral standards
Perfect For
When you need to enforce content policies across thousands or millions of AI-generated responses without hiring an army of human reviewers.
Healthcare, finance, legal, and education sectors where AI outputs must comply with specific regulatory requirements and professional standards.
Ensuring AI-generated customer communications consistently reflect company values, tone standards, and ethical commitments across all touchpoints.
Systematically evaluating model outputs against known failure modes, bias categories, and safety requirements with explicit, testable criteria.
Skip It When
Straightforward factual lookups, calculations, or data transformations where alignment concerns are minimal and the overhead of critique-revise is unnecessary.
Real-time systems where the additional inference passes required for critique-revise loops would create unacceptable delays for users.
Creative writing, art direction, or opinion pieces where rigid constitutional principles might suppress legitimate creative expression or diverse viewpoints.
Use Cases
Where Constitutional AI delivers the most value
AI Safety Testing
Define safety constitutions and systematically evaluate model outputs against them, creating reproducible safety benchmarks that improve with each principle added.
Policy Compliance
Encode regulatory requirements as constitutional principles and have the AI self-check every response for compliance before it reaches end users.
Educational AI Tutors
Define constitutions that ensure AI tutors encourage critical thinking, avoid giving direct answers to homework, and promote academic integrity while remaining helpful.
Customer-Facing Chatbots
Ensure chatbot responses consistently reflect brand values, avoid making promises the company cannot keep, and handle sensitive topics with appropriate care.
Healthcare AI Assistants
Constitutional principles that enforce medical disclaimer requirements, prevent diagnostic claims, maintain patient privacy standards, and always recommend professional consultation.
Bias Auditing
Define fairness principles as a constitution and systematically evaluate model outputs for demographic bias, stereotyping, and unequal treatment across protected categories.
Where Constitutional AI Fits
CAI bridges human oversight and scalable AI alignment
You do not need to train a model from scratch to use Constitutional AI principles. The critique-revise pattern works as a prompting technique right now: write a set of principles relevant to your use case, include them in your system prompt, and instruct the model to evaluate its own responses against those principles before delivering them. This “constitutional prompting” approach gives you many of CAI’s benefits — consistent policy enforcement, transparent reasoning about safety decisions, and auditability — without any model training. Always verify the model’s self-critique is actually catching the issues you care about.
A practical starting point: write 5–10 principles specific to your domain, test them against known failure cases, and iterate. Good constitutions are specific enough to be actionable (“do not recommend medications without advising professional consultation”) rather than vague (“be helpful”). The more concrete your principles, the more reliably the model can evaluate its own compliance.
Related Techniques
Explore complementary alignment and self-improvement approaches
Align Your AI Interactions
Explore principle-based prompting to make your AI outputs safer and more aligned, or build structured prompts with our tools.