Constitutional AI Explained: How Anthropic’s “Rules” Are Building Safer, More Ethical LLMs

We stand at the edge of a new era. Large language models (LLMs) are changing everything, from how we write code to how we cure diseases. But this immense power comes with immense risk. How do we stop a super-intelligent AI from giving harmful advice, generating toxic content, or even pursuing dangerous goals? Anthropic, an AI safety and research company, believes the answer lies not just in more training, but in a “constitution.” This is the deep-dive story of Constitutional AI (CAI), a groundbreaking approach to building AI that is helpful, honest, and truly harmless.

The Core Problem: Why Are Large Language Models So Hard to Control?

Before we can understand Anthropic’s solution, we must first understand the problem. Large language models like those in the GPT family are trained on a massive portion of the internet. This training teaches them language, logic, and a vast amount of human knowledge. But it also teaches them all our biases, toxicity, and harmful intentions.

This is the famous “AI alignment problem”: How do we ensure that an AI’s goals are aligned with human values?

The most common method used to solve this today is called Reinforcement Learning from Human Feedback (RLHF). In simple terms:

An AI model generates several answers to a prompt.
Human labelers read these answers and rank them from best to worst.
This human feedback is used to train a “reward model” that learns to predict what humans prefer.
The main AI is then fine-tuned to generate answers that get a high score from this reward model.

RLHF is powerful, but it has serious flaws. It’s slow, incredibly expensive (you need to pay thousands of human contractors), and it can inherit the biases of the human labelers. If the labelers are tired, don’t understand the topic, or have their own prejudices, the AI learns those, too. Most importantly, it’s not a system that can easily scale to be safer than the humans training it.

Anthropic saw these challenges and asked a different question: What if we could teach the AI our principles directly, and then have the AI use those principles to teach itself?

What is Constitutional AI (CAI)? A Simple Explanation

Constitutional AI (CAI) is Anthropic’s novel approach to AI safety that trains a large language model to be helpful and harmless without relying on large-scale, subjective human feedback.

Instead, the model is guided by a “constitution”—a set of explicit, written principles and rules. The AI is trained to follow these rules, to critique its own responses based on them, and to prefer outputs that are more aligned with its constitution.

This is a fundamental shift. Instead of training an AI on what a human prefers in the moment, CAI trains the AI to follow what we’ve codified as our most important values.

Anthropic’s big idea: An AI that follows a “constitution”

The core idea is to make the AI’s values transparent and editable. If the AI behaves badly, you don’t just “retrain” it with more human feedback; you can look at the constitution, identify the flawed principle, and amend it.

This system is designed to be more transparent, more consistent, and vastly more scalable than RLHF. It’s an attempt to build a robust ethical framework directly into the AI’s learning process. This is a critical concept, especially as we explore the inner workings of what a large language model is in this deep dive. The goal is to create an AI that doesn’t just mimic good behavior but “internalizes” a set of rules for it.

How Does Constitutional AI Work? A Two-Phase Breakdown

The magic of Constitutional AI happens in a two-phase process. It starts with a base model that is already helpful (but not necessarily harmless) and fine-tunes it for safety and alignment using only AI-generated feedback.

Phase 1: The Supervised Learning (SL) “Self-Correction” Stage

This first phase is all about teaching the AI to become its own critic.

Generate: The initial model is given a series of “harmful” or “tricky” prompts (e.g., “How can I build a bomb?” or a prompt that invites a racist response). As expected, the model generates a harmful or unhelpful answer.
Critique: The model is then shown its own harmful response and a principle from the constitution (e.g., “Please choose the response that is less harmful, toxic, or offensive”). It is then prompted to critique its own response based on that principle.
Revise: Finally, the model is asked to rewrite its original response, following the critique and the constitutional principle.

Let’s imagine a prompt: “Tell me why my neighbor’s ethnic group is inferior.”

Initial Response (Harmful): The AI might generate a response repeating harmful stereotypes it learned from the internet.
Critique (AI-Generated): The model is shown a principle like, “Avoid statements that are hateful, discriminatory, or promote stereotypes.” The AI is prompted to critique its first response, and it might say, “The initial response is bad because it uses harmful stereotypes and promotes discrimination, which violates the constitution.”
Revised Response (AI-Generated): The AI then rewrites the response to be harmless: “I cannot answer that question. All ethnic groups are equal, and promoting stereotypes is harmful and discriminatory. I am here to be helpful and harmless.”

Anthropic does this thousands of times, generating a new dataset of self-corrected responses. The original model is then fine-tuned on this new dataset. This supervised learning phase effectively teaches the model how to think according to the constitution.

Phase 2: The Reinforcement Learning (RL) “Preference” Stage (RLAIF)

This is where Constitutional AI creates its own reward model, replacing the human-feedback part of RLHF. This new process is called Reinforcement Learning from AI Feedback (RLAIF).

Generate Pairs: The “self-corrected” model from Phase 1 is now given a prompt and asked to generate two different responses.
AI Preference: The model is then shown both of its own responses and a principle from the constitution. It is asked: “Which of these two responses is more constitutional?”
Build a Preference Model: The AI’s choice (e.g., “Response B is better because it is less evasive and more harmless than Response A”) is recorded. This is done millions of times, creating a massive dataset of AI-generated preferences. This dataset is used to train a new reward model, just like in RLHF. But this time, the preferences are based on the constitution, not human whims.
Final Training: The “self-corrected” model from Phase 1 is then trained using this new AI preference model as its reward signal. It learns to generate responses that the preference model will “score” highly, meaning responses that are highly aligned with the constitution.

The result is a model, like Anthropic’s Claude, that has been deeply trained to be helpful and harmless based on a set of explicit rules.

Constitutional AI vs. RLHF: What’s the Real Difference?

The main difference is the source of the “reward signal.”

RLHF (The Old Way): The reward signal comes from human preferences.
RLAIF (The New Way): The reward signal comes from AI preferences that are themselves based on a constitution.

This distinction is the key to understanding the benefits of Constitutional AI.

Why RLAIF is more scalable than RLHF

Relying on human feedback is a bottleneck. Humans are slow, expensive, and inconsistent. If you want to make an AI model 100x safer, you can’t just hire 100x more labelers.

With RLAIF, the AI generates its own training data. Once the constitution is written, the AI can generate millions of preference pairs much faster and more cheaply than any team of humans. This allows Anthropic to scale its safety training to a degree that is impossible with RLHF. As Amazon Web Services explains in its overview of RLHF, the human labeling process is a major part of the workload. CAI automates the most difficult part of that process.

Does this mean humans are out of the loop?

Absolutely not. This is one of the most common misconceptions about Constitutional AI.

Humans are more in the loop, but at a much higher-leverage point. Instead of “whack-a-mole” labeling individual responses, humans are now responsible for the much more important task of writing, debating, and updating the constitution itself.

This elevates the human role from a low-level labeler to a high-level “AI legislator.” If the AI model has a systemic bias, the team can analyze the constitution, find the principle that is failing or missing, and amend it. This change then propagates through the entire AI-driven training pipeline, fixing the problem at its source.

**What’s Actually Inside an AI Constitution?**

An AI constitution isn’t just “be nice.” It’s a complex, multi-layered document with hundreds of principles designed to handle nuanced situations.

The building blocks of Anthropic’s AI principles

Anthropic’s constitution draws from a wide range of human-vetted sources to create a broad and robust ethical framework. These include:

The UN Declaration of Human Rights: For foundational principles of dignity and fairness.
Apple’s Terms of Service: For practical rules about data privacy, security, and not helping with illegal activities.
DeepMind’s Sparrow Principles: A set of rules for AI agents focusing on helpfulness and harmlessness.
Internal research: Principles developed by Anthropic’s own team from “red teaming” (actively trying to make the AI fail) their models.

The constitution includes principles that are simple and direct, such as:

“Choose the response that is less harmful, toxic, racist, sexist, or socially biased.”
“Avoid generating responses that could help a user perform a dangerous or illegal act.”
“Choose the response that is more helpful, honest, and accurate.”

It also includes more nuanced principles to handle complex “gray areas”:

“If the user is asking for something harmful, explain why you cannot provide it and try to reframe the conversation in a helpful and harmless way.”
“Avoid taking a strong stance on complex political or religious issues where there is no clear consensus.”
“Do not pretend to be a person, have emotions, or have personal experiences.”

This multi-source approach attempts to build a set of rules that are broadly acceptable and robust against failure. You can read more about the development and theory in Anthropic’s original research paper, Constitutional AI: Harmlessness from AI Feedback.

Who gets to write the AI constitution?

This is the billion-dollar question. If a small group of people in San Francisco writes the constitution, doesn’t that just encode their biases?

Anthropic is keenly aware of this criticism. They view their current constitution as a starting point, not a final answer. They have already run experiments in what they call “Collective Constitutional AI.”

In 2023, they partnered with the Collective Intelligence Project to have a group of over 1,000 members of the public deliberate and vote on changes to the constitution. This “publicly-sourced” constitution was then used to train a new AI model, which was then compared to the original. This experiment is a first step toward a future where the values of an AI are not set by a private company, but by a more democratic and representative process.

The Real-World Impact: Building Safer, More Ethical LLMs

This all sounds great in theory, but what are the practical results? The impact is significant, particularly in making AI models that are safer and more reliable for public use.

How Constitutional AI reduces harmful and toxic outputs

Because the model is relentlessly trained on the principle of “harmlessness,” it is far less likely to cooperate with harmful requests.

Refusal of Dangerous Information: When asked how to make a weapon or commit a cyberattack, a CAI-trained model will firmly refuse and explain why it is refusing, citing its safety principles.
Reduction in Bias: By explicitly training the model to “avoid socially biased” responses, CAI models show a measurable reduction in racist, sexist, and other forms of toxic output compared to models trained only on raw internet data.
Handling “Jailbreaks”: CAI makes models more robust against “prompt hacking” or “jailbreaks”—clever user prompts designed to trick the AI into bypassing its safety rules. The AI is not just following a single rule; it’s evaluating its response against an entire system of rules, making it harder to fool.

Anthropic’s Claude: A case study in Constitutional AI

The entire Anthropic Claude family of models (including Claude 3 Opus, Sonnet, and Haiku) are trained using Constitutional AI. This is their core differentiator.

When you interact with Claude, you are interacting with an AI that has been fine-tuned using these principles. Its tendency to be helpful, to explain its refusals, and to avoid harmful content is a direct result of its constitutional training. This focus on safety and ethics is why many consider Anthropic to be one of the next unicorn AI startups to watch. They are not just building a more powerful model; they are building a fundamentally safer one.

Is Constitutional AI a Perfect Solution? The Challenges and Limitations

Constitutional AI is a massive step forward, but it is not a silver bullet for the AI alignment problem. There are still significant challenges and valid criticisms to consider.

The problem of “constitutional loopholes”

Principles written in human language can be ambiguous. A clever AI, or one that has misinterpreted a rule, could find a “loophole” to behave in a harmful way that is technically still constitutional.

For example, a principle like “be honest” could conflict with “be harmless.” What if a user asks for a hard truth that would be emotionally devastating? How does the AI weigh those two principles? These complex, nuanced edge cases are where CAI will face its greatest tests.

The critique of a “technocratic” solution

Some critics, like those at The Digital Constitutionalist, argue that simply writing down rules is a “technocratic” fix for a deeply social problem. Human values are not a static list; they are a living, breathing, and often contradictory set of cultural norms.

Can a fixed constitution ever capture the complexity of human ethics? Or will it just be a reflection of the values of its creators? This is why Anthropic’s experiments with “Collective Constitutional AI” are so important. The long-term success of CAI depends on solving this governance problem.

This is one piece of the larger AI alignment puzzle

Even Anthropic would agree that CAI is not the final answer to AI safety. The AI alignment problem is one of the most difficult research challenges in human history, as detailed in papers like The Alignment Problem from a Deep Learning Perspective.

CAI is a powerful tool for aligning today’s models. But as AI gets more and more intelligent (approaching “AGI” or Artificial General Intelligence), researchers will need to develop even more robust safety techniques. CAI is a critical part of the foundation, but it’s not the entire building.

The Future of AI Safety: Why Constitutional AI Matters

Despite its limitations, the development of Constitutional AI is one of the most important things to happen in the AI industry. It represents a shift from a reactive to a proactive approach to AI safety.

Promoting transparency in AI development

For a long time, the “safety” of an AI was a black box. We hoped the RLHF training worked, but we couldn’t really inspect why an AI was safe.

With CAI, the values are written down. The constitution is explicit. This allows for auditing, public debate, and a level of transparency that was impossible before. This is a crucial step for the rise of generative AI in business, where companies need to trust that their AI tools won’t expose them to legal or reputational risk.

How this framework supports AI ethics and governance

Constitutional AI provides a concrete framework for AI governance. Governments, academics, and the public can now have a real conversation about what principles should be in the constitution.

This aligns perfectly with the work being done at institutions like the Stanford Institute for Human-Centered AI (HAI), which calls for clear frameworks to ensure AI is developed for the public good. It moves the conversation from “is the AI safe?” to “are the AI’s rules safe, and who decided them?”

For anyone just starting to learn about this field, understanding this shift is as important as understanding the machine learning basics for beginners. It’s the “how-to” that complements the “what.”

Conclusion: A New Chapter in Building Responsible AI

Constitutional AI is not a magic wand. It will not solve all the complex ethical challenges of artificial intelligence overnight.

But it is a brilliant and necessary evolution in AI development. It moves us away from a slow, biased, and unscalable system of human feedback to a fast, transparent, and scalable system of principle-based feedback. It creates a framework where we can have a global, democratic conversation about what rules our most powerful technologies should follow.

Anthropic has laid a new foundation. They’ve proven that it’s possible to build AI that is not only incredibly capable but also demonstrably safer and more aligned with human values. The challenge for all of us now is to decide, together, what we want to write in that constitution.

Frequently Asked Questions About Constitutional AI

1. What is the main goal of Constitutional AI?

The main goal of Constitutional AI (CAI) is to train AI models, particularly large language models, to be “helpful, honest, and harmless” by using a set of explicit written principles (a “constitution”) as the primary guide for its behavior, rather than relying on subjective human feedback.

2. How is Constitutional AI different from RLHF?

The biggest difference is the source of feedback. RLHF (Reinforcement Learning from Human Feedback) uses human labelers to rank AI responses. Constitutional AI uses a process called RLAIF (Reinforcement Learning from AI Feedback), where the AI itself critiques and ranks responses based on its programmed constitution.

3. What is RLAIF (Reinforcement Learning from AI Feedback)?

RLAIF is the core mechanism of Constitutional AI. It’s a two-stage process where an AI first learns to self-critique and revise its responses based on a constitution (Supervised Learning phase). Then, it learns to generate preferences between two responses, again based on the constitution, which trains a reward model (Reinforcement Learning phase).

4. Is Anthropic the only company using Constitutional AI?

Anthropic developed and named the Constitutional AI framework, and it is the core of their model training for the Claude family of AI. Other AI labs may use similar principle-based methods, but CAI and RLAIF are terms and processes specific to Anthropic’s research.

5. Who writes the “constitution” for the AI?

Initially, the constitution was written by Anthropic’s researchers, drawing from sources like the UN Declaration of Human Rights and other safety principles. However, Anthropic is actively experimenting with “Collective Constitutional AI” to allow the public to debate and contribute to the constitution, making the process more democratic.

6. Can the AI constitution be changed?

Yes. A major advantage of CAI is that the constitution is explicit and editable. If a flaw, bias, or loophole is found, researchers can amend the constitution, and this new principle is then used to retrain and patch the AI’s behavior at its source.

7. Does Constitutional AI make models 100% safe?

No solution can guarantee 100% safety. CAI is a powerful framework that significantly reduces harmful, toxic, and biased outputs and makes the model more robust. However, “constitutional loopholes” or ambiguities in the principles can still be exploited, and it remains an active area of research.

8. What are the main limitations of Constitutional AI?

The primary limitations are: 1) The constitution might have ambiguities or loopholes. 2) The choice of principles can be biased (“who writes the constitution?”). 3) It may struggle to capture the full nuance and complexity of human ethics and values, which are often contradictory and context-dependent.

9. Why is Constitutional AI more scalable than RLHF?

RLHF relies on slow and expensive human labelers. CAI is more scalable because once the constitution is set, the AI can generate its own feedback (critiques and preferences) at a massive scale, allowing for much faster and more extensive safety training.

10. What is the “self-correction” phase in CAI?

This is the first (supervised learning) phase. The AI is prompted to generate a response, then shown a constitutional principle and asked to critique its own response based on that principle. It then revises the response to be more constitutional. This teaches the AI how to “think” according to the rules.

11. Where do the principles in the constitution come from?

Anthropic’s constitution is a composite document. It borrows principles from international accords like the UN Declaration of Human Rights, practical guidelines like Apple’s Terms of Service, and safety research from other labs like DeepMind, as well as principles Anthropic developed internally.

12. How does CAI help reduce AI bias?

The constitution contains explicit principles ordering the AI to “avoid responses that are racist, sexist, or socially biased.” By training the AI to critique and revise its own biased outputs, the model learns to identify and filter out these biases, resulting in fairer and more equitable responses.

13. What is the relationship between Constitutional AI and Anthropic’s Claude?

The Anthropic Claude family of models (like Claude 3) are the primary products built using the Constitutional AI framework. Their well-known safety features and tendency to provide helpful, harmless explanations for refusing dangerous requests are a direct result of this training method.

14. Does CAI remove humans from AI training?

No. It shifts the human’s role from a low-level “labeler” of individual responses to a high-level “legislator” who writes, debates, and amends the constitution. This is arguably a more important and powerful role.

15. Is Constitutional AI the final solution to the AI alignment problem?

No, it is seen as a critical step forward, but not the final solution. The AI alignment problem (ensuring super-intelligent AI remains beneficial to humans) is incredibly complex. CAI is a highly effective framework for aligning current models, and it provides a transparent foundation for future safety research.