Definition: Constitutional AI (CAI) is an alignment technique developed by Anthropic that trains AI models to evaluate, critique, and improve their own responses against a written set of principles, a "constitution," rather than relying solely on large-scale human feedback. The approach was published on December 15, 2022, and it has since become the foundational training method behind Anthropic's Claude model family.
Constitutional AI addresses a core bottleneck in AI safety. Training models with human feedback (RLHF) takes tens of thousands of human preference labels, is expensive to scale, and bakes in implicit values that are hard to examine or update. Constitutional AI replaces much of that human labor with a written document, the constitution, that states the principles the model should follow. The model then critiques its own outputs against those principles.
TL;DR: Constitutional AI (CAI) is Anthropic's method for training a model to critique and revise its own answers against a written constitution instead of relying on human preference labels. Published December 15, 2022, it now anchors Claude's alignment. The same idea, written rules for AI behavior, is how teams govern AI agents in Taskade.
What Is Constitutional AI?
Constitutional AI is a training method where a model improves its own behavior by judging its answers against a short written set of principles, the constitution, rather than against thousands of human "which is better?" votes. The principles do the steering, so the values guiding the model are visible and editable instead of buried in aggregated human preferences.
Instead of asking thousands of human raters "which response is better?", you give the AI model a set of written principles and ask it to judge its own responses against them.
This matters because the principles are explicit (anyone can read them), auditable (researchers and the public can examine what values are encoded), and updatable (the constitution can evolve as understanding improves). With RLHF, the values are implicit in the aggregate preferences of human raters. That makes them hard to inspect, hard to change, and hard to keep consistent across thousands of individual judgments.
Anthropic's original paper demonstrated that Constitutional AI produces models that are both more helpful and more harmless than RLHF-trained alternatives, while also being more transparent about the values that guide their behavior.
How Constitutional AI Works
Constitutional AI works in two phases. In phase one the model writes a response, critiques it against the constitution, and rewrites the better version. In phase two a separate AI evaluator, also guided by the constitution, picks the stronger of two responses, and those AI-made choices replace human preference labels in reinforcement learning.
The heart of the method is a loop: every draft answer is checked against a written principle and rewritten until it complies. The diagram below shows that critique-and-revise cycle.
The training process has two distinct phases that work together to shape model behavior:
Phase 1: Supervised Learning (Critique and Revision)
In the first phase, the AI model generates responses to a set of prompts, including prompts designed to elicit harmful, biased, or unhelpful outputs. The model then critiques its own responses by evaluating them against specific constitutional principles.
For example, the constitution might include a principle like: "Choose the response that is most helpful while being honest and avoiding harm." The model reads its initial response, compares it to this principle, writes a critique identifying where the response falls short, and then generates a revised response that better aligns with the principle.
This critique-and-revision cycle can repeat multiple times, with the model progressively improving its output. The revised responses are then used as supervised training data. The model learns to produce the improved versions directly, without needing the intermediate critique step.
The key insight is that the AI model is doing the labor that human raters would traditionally perform. Instead of a human saying "Response A is better than Response B," the model uses the constitution to make that judgment itself.
Phase 2: Reinforcement Learning from AI Feedback (RLAIF)
The second phase uses reinforcement learning from AI feedback (RLAIF) instead of the traditional reinforcement learning from human feedback (RLHF). The model generates pairs of responses to the same prompt, then a separate AI evaluator (guided by the constitution) judges which response better aligns with the principles.
These AI-generated preference labels replace the human preference labels used in RLHF. The model is then fine-tuned with reinforcement learning to produce responses that the constitutional evaluator prefers. This creates a feedback loop where the constitution drives model improvement at scale.
The result is a model that has internalized the principles of its constitution without requiring tens of thousands of individual human judgments. Human oversight stays essential. Humans write the constitution, evaluate the training outcomes, and update the principles. What goes away is the bottleneck of per-response human labeling.
Constitutional AI vs RLHF
Constitutional AI and RLHF both teach a model what "good" looks like, but they source the signal differently. RLHF learns from human preference rankings on individual responses. Constitutional AI learns from a written set of principles plus AI self-critique. The result is cheaper to scale, easier to audit, and more consistent, while still resting on human-authored rules.
| Dimension | Constitutional AI (CAI) | RLHF (human feedback) |
|---|---|---|
| Training signal | Written principles + AI self-critique | Human preference rankings |
| Scalability | High, automated evaluation | Lower, human raters per comparison |
| Transparency | High, principles are published and auditable | Lower, preferences implicit in rater judgments |
| Cost | Lower per label, AI-generated labels | Higher, human labeling at scale |
| Updatability | Edit the constitution document | Retrain with new human raters |
| Consistency | Same principles applied uniformly | Subject to inter-rater disagreement |
| Human oversight | Humans write and audit the constitution | Humans label individual responses |
Constitutional AI does not remove humans from the loop. People design the constitution, evaluate the model's behavior against it, and update the principles over time. The technique shifts human effort from labeling individual responses to defining and refining principles, a more scalable and transparent form of oversight.
Why Constitutional AI Matters
Constitutional AI matters because it makes the values guiding a model visible, editable, and applied the same way every time. Written principles scale to far more outputs than human raters can review, they can be read and challenged in public, and they can be revised in one place when understanding changes. That combination, scalability plus transparency, is hard to get from human preference labels alone.
Scalability. As AI models grow larger and are deployed in more contexts, the number of possible outputs that need evaluation grows fast. Constitutional AI scales evaluation by replacing per-response human judgment with principle-based AI self-assessment.
Transparency. The constitution is a public document. Anyone can read it, critique it, and suggest improvements. That is a clear advance over RLHF, where the values encoded in human preferences are hard to extract and examine.
Consistency. A written constitution applies the same principles uniformly across all training examples. Human raters, by contrast, disagree with each other and vary in their judgments over time and across cultures.
Adaptability. When values or understanding evolve, the constitution can be updated directly. Anthropic has shown this in practice. Claude's constitution grew from roughly 2,700 words in 2023 to roughly 23,000 words in the January 2026 update, shifting from rigid rules toward reason-based principles that explain the logic behind each guideline.
The constitution hierarchy. Claude's constitution sets a clear priority order: (1) being safe and supporting human oversight, (2) behaving ethically, (3) following Anthropic's guidelines, and (4) being helpful. This order is notable because many AI systems optimize mainly for helpfulness, which can lead to compliance with harmful requests. By placing safety above helpfulness, Constitutional AI produces models that can refuse harmful instructions while staying transparent about why.
CLAUDE'S CONSTITUTION - PRIORITY ORDER (top wins ties)
┌───────────────────────────────────────────────────────────┐
│ 1 Be safe + support human oversight ◄── highest │
├───────────────────────────────────────────────────────────┤
│ 2 Behave ethically │
├───────────────────────────────────────────────────────────┤
│ 3 Follow Anthropic's guidelines │
├───────────────────────────────────────────────────────────┤
│ 4 Be helpful ◄── lowest │
└───────────────────────────────────────────────────────────┘
When two principles conflict, the higher rank wins. Safety
outranks helpfulness, so the model can decline a harmful ask.
Constitutional AI in Practice
You already use a version of this idea whenever you write instructions for an AI tool. Every time you tell an assistant what it should and should not do, you are giving it a tiny constitution. The same principle that aligns a frontier model, clear written rules over implicit preferences, is what makes an everyday AI agent predictable and safe to hand real work.
The principles behind Constitutional AI reach beyond model training into how AI tools are built for everyday use. When a team gives an AI agent specific behavioral guidelines (always cite sources, never share confidential data, escalate legal questions to a human), they are writing a form of constitution for that agent.
In Taskade, this pattern shows up in how teams configure AI agents with custom instructions, behavioral boundaries, and domain-specific guidelines. A system prompt that tells an agent "you are a customer support specialist who should never make promises about pricing" works as a lightweight constitution, a written set of rules that shapes every one of the agent's responses.
The parallel is not exact. System prompts are instructions, not a training method. But the underlying insight is the same: explicit, written principles produce more predictable, auditable, and trustworthy AI behavior than implicit preferences or unconstrained generation. Teams that invest in clear agent instructions inside their Workspace DNA are applying the same philosophy Constitutional AI brought to model training, making values visible and enforceable. The same instinct underlies prompt engineering: say what you want clearly, and you get behavior you can trust.
As AI agents become more autonomous, taking actions, managing workflows, and reaching external systems through protocols like MCP and agent-to-agent communication, constitution-style governance matters more, not less. Agents that operate independently need clear boundaries, and written principles are the most transparent way to define them. This is also why AI alignment and AI safety sit at the center of how frontier models are trained today.
Put a Constitution Around Your Own AI
You don't have to train a model to apply this idea. You can write the rules and put them to work the same day.
Build a client portal in Taskade Genesis where a support agent answers customer questions inside the boundaries you set. Describe the app in plain English, then give the agent its constitution in its instructions: answer from approved docs only, never quote a price, escalate billing and legal questions to a human. Your clients log in, ask their questions, and get consistent on-brand answers around the clock. The agent runs on 15+ frontier models with 34 built-in tools, so it can search your knowledge base and draft replies, but it stays inside the rules you wrote. When your policy changes, you edit one set of instructions and every reply updates, the same "edit the constitution, not the labels" move at the scale of one business. Start building free →
Frequently Asked Questions About Constitutional AI
What Is the Difference Between Constitutional AI and RLHF?
RLHF trains models using human preference labels. Human raters compare pairs of responses and indicate which is better. Constitutional AI replaces most of this human labeling with AI self-evaluation guided by written principles. The result is more scalable, more transparent, and more consistent, while still relying on human-authored principles as the foundation. See RLHF for the human-feedback side of the comparison.
Does Constitutional AI Make AI Perfectly Safe?
No. Constitutional AI significantly improves alignment but does not guarantee absolute safety. Anthropic classifies models on AI Safety Levels (ASL-1 through ASL-5) and maintains a Responsible Scaling Policy with progressively stricter safety requirements as model capabilities increase.
Can I Read Claude's Constitution?
Yes. Anthropic publishes Claude's constitution publicly. The 2026 version is approximately 23,000 words and provides detailed reasoning behind each principle rather than stating rules without context.
What Does RLAIF Stand For?
RLAIF stands for Reinforcement Learning from AI Feedback. It is the second phase of Constitutional AI training, where AI-generated preference labels (based on the constitution) replace human-generated preference labels used in traditional RLHF.
Is Constitutional AI Used by Other Companies?
The technique was published openly by Anthropic and has influenced alignment research broadly. Other labs use variations of the approach, including principle-based reward modeling and self-refinement techniques. Constitutional AI as a branded methodology is most closely associated with Anthropic and the Claude model family.
How Does Constitutional AI Relate to AI Agent Safety?
Constitutional AI establishes the foundation for safe AI agent behavior at the model level. When you build AI agents on models trained with Constitutional AI, those agents inherit the model's alignment properties. Custom agent instructions and system prompts then add a layer of task-specific governance on top of the model's base alignment.
What Are the Two Phases of Constitutional AI?
Phase one is supervised: the model critiques its own response against the constitution and rewrites a better version, and those revisions become training data. Phase two is reinforcement learning from AI feedback (RLAIF): a constitution-guided evaluator ranks pairs of responses, and those AI-made choices replace human preference labels.
Can I Apply Constitutional AI Without Training a Model?
Yes, in a lightweight form. Writing clear, explicit rules in an AI agent's instructions, what it should do, what it must never do, when to escalate, applies the same principle: visible written rules over implicit preferences. It is governance at the prompt level, not a training method, but it produces more predictable behavior for the same reason.
Related Concepts
RLHF: Reinforcement learning from human feedback, the human-labeled approach Constitutional AI scales beyond
Reinforcement Learning: The training paradigm Constitutional AI builds on with its RLAIF phase
Fine-Tuning: How a base model is shaped for behavior, the stage where constitutional revisions become training data
AI Alignment: The broader goal of making model behavior match human intent, which Constitutional AI advances
AI Safety and Alignment: How labels and policies govern increasingly capable models
Large Language Models: The AI models trained using Constitutional AI techniques
Machine Learning: The broader field that encompasses Constitutional AI's training methodology
AI Agents: Autonomous systems whose behavior is shaped by constitution-style principles and instructions
Prompt Engineering: Crafting effective instructions that work within constitutional boundaries
System Prompt: Agent-level instructions that function as lightweight constitutions for specific tasks