What Is Constitutional AI?

Constitutional AI is an approach for training harmless and helpful AI models. This method uses a formal “constitution,” which is a set of principles to guide the AI’s behavior. The AI learns to critique its own responses against these rules, aligning its outputs with human values without constant human supervision.

Constitutional AI. It’s a term that gets thrown around. It’s basically a method for training AI models to be, well, helpful and not harmful. The whole thing uses a “constitution”, which is really just a fancy word for a set of rules or principles that guide how the AI should act. The AI literally learns to check its own work against these rules, which is supposed to get its outputs in line with human values without a person having to look over its shoulder every second. The reason anyone cares about what is constitutional AI is because it’s a stab at figuring out how we can build safer AI.

It’s pretty important. As these AI systems get plugged into everything, making sure they have some kind of ethical compass is, to put it mildly, paramount. It’s not just about a simple filter that blocks bad words; it’s about trying to bake a real sense of harmlessness right into the machine’s core logic. Those principles in the constitution? They’re the whole foundation. They are the final say in how the AI behaves.

A Quick Look at Constitutional AI Principles

When you get down to it, Constitutional AI is a major shake-up for AI alignment. The biggest headache in making smart AI has always been this “AI alignment” problem, making sure the machine’s behavior actually matches what humans want and what we consider ethical. The old way, Reinforcement Learning from Human Feedback (RLHF), means you have a bunch of humans sitting there, clicking “good” or “bad” on thousands of AI answers. It works, sort of. But it’s slow, costs a fortune, and you end up just baking the random biases of the human labelers into these powerful AI systems. Constitutional AI is a clever way around all that. It just gives the job of the human labeler to another AI, and that AI judge makes its calls based on that list of principles, the constitution.

So, the whole idea behind Constitutional AI is to just write down the AI’s value system. Make it clear, make it something you can see and edit. Instead of these values being mysteriously absorbed from a giant, messy pile of human opinions, they are literally written out in a document the AI has to follow. And this isn’t just a “don’t say this” list. It’s a mix of positive and negative principles. For instance, a rule might be “pick the answer that is most helpful and honest,” or on the flip side, “don’t give answers that are toxic or racist.” During training, the AI writes something, then it’s forced to critique its own work using these principles, and then it has to rewrite the answer to be better. This whole self-correction loop is the key; it lets the AI systems make their own training data, teaching themselves to be better. It’s brilliant, really, because it can scale up in a way that just hiring more people never could.

• Written Down Values: The whole core of constitutional AI is having a written list of principles for the model to follow. Simple as that.
• Less Human Babysitting: It cuts down on how much you need people constantly checking the AI’s work for safety stuff.
• Scales Up Like Crazy: Using an AI to check on another AI means you can scale up the alignment for even the most massive AI systems.
• You Can See and Change It: The constitution isn’t locked in a vault. You can review it, update it. It makes the ethics transparent.
• It Teaches Itself: The AI learns to check and fix its own mistakes based on its guiding principles.
• Goal: Don’t Be Evil: A big objective is to train AI systems that are just built from the ground up to be helpful and safe.
• Anthropic’s Brainchild: This whole thing came from the AI safety company Anthropic.
• More Than Just a Filter: It’s a deeper approach than just blocking words; it tries to teach the AI what ethical behavior even means.

Following Anthropic’s Playbook for AI Systems

The story of Constitutional AI going from a neat idea to a real thing is all tangled up with Anthropic, the AI safety lab. They dropped the idea on the world in a 2022 paper called “Constitutional AI: Harmlessness from AI Feedback.” They were trying to get around the big problems with RLHF, how much it costs, how biased it could be. The team at Anthropic had a theory: what if an AI model could learn to be safe just by reading a list of principles instead of having a human smack its digital wrist every time it messed up? This was the big idea. It paved the way for more automated, scalable ways to align these incredibly powerful AI systems with what we actually want them to do.

The system Anthropic came up with is very methodical. It’s got two parts: a supervised learning phase, and then a reinforcement learning phase. It’s all designed to drill the constitution’s principles into the language model. You start with a model that’s pretty helpful but maybe not totally safe. Then, through this two-part process, you polish it into an AI that is both helpful and harmless. They called their method Reinforcement Learning from AI Feedback (RLAIF), and it’s the engine that makes Constitutional AI go. Anthropic basically built a closed loop where the AI trains itself on safety, because all the feedback comes from another AI that’s just following the constitution. A huge step. It proved you could align complex AI systems with ethical rules without needing a proportional army of humans.

The Two-Phase Training Thing

First up is the supervised learning (SL) stage. You take a pre-trained language model and give it prompts, some of them designed to get it to say bad things. The AI gives a response. Then, and this is the important part, you give the same AI its own response, the original prompt, and one of the principles from the constitution. You tell it to critique its answer based on that rule and then rewrite it. For example, if the AI was cagey, a principle like “be helpful and honest” would make it revise its answer to be more straightforward. This whole critique-and-rewrite thing happens over and over with different principles, which creates a big dataset of the AI correcting itself. That new data is then used to fine-tune the original model. So this first part is basically teaching the AI to understand what the constitutional principles even mean.

The second stage is reinforcement learning (RL). This is where the alignment really sticks. In this phase, called RLAIF, the model from stage one spits out pairs of answers for different prompts. Then another AI, the “preference model,” has to look at both answers and decide which one is better according to the whole constitution. Is it more harmless? Is it more helpful? Less biased? This generates a giant pile of AI-labeled preferences. This preference model, now a stand-in for the constitution, is used to train the final AI model with reinforcement learning. The AI gets a reward for making responses the preference model likes, which optimizes its behavior to perfectly match the constitution. It’s a whole process that ensures the final AI systems aren’t just dodging bad answers but are actively trying to be good, based on their foundational principles.

• RLAIF vs. RLHF: Constitutional AI is all about AI feedback (RLAIF); the old way is all human feedback (RLHF).
• Where’s the feedback from?: In RLAIF, it comes from an AI using a list of explicit principles. In RLHF, it’s just what some person thinks.
• Scalability: RLAIF is way, way more scalable. No need for thousands of human work hours.
• Consistency: An AI judge is probably more consistent than a bunch of different human labelers who all have their own opinions.
• Quick Changes: You can change the constitution and retrain the AI systems super fast. Much faster than getting a whole new human feedback project going.
• Bias problem: It’s not a perfect fix for bias, but RLAIF doesn’t directly hard-code the weird biases of specific human raters.
• The Goal: Both want to align AI, but Constitutional AI uses a written, editable rulebook to do it.
• Transparency: The principles for RLAIF are right there for anyone to see. The reasons a human labeler in RLHF chose one thing over another? Who knows.

From Idea to Reality: The Claude Models

The most famous example of what is constitutional AI in the real world is Anthropic’s family of models, which they call Claude. It’s not just some lab experiment; it’s a whole suite of commercial AI systems built from the start using this Constitutional AI framework. Every version of Claude has its behavior shaped by its constitution. So when you talk to Claude, its answers are guided by principles pushing it towards being helpful, honest, and safe. For example, if you ask it how to do something dangerous, it’s trained not just to say no, but to explain why it’s saying no, by referencing its commitment to safety. That’s a direct outcome of the RLAIF training, where it was rewarded for doing exactly that.

The effect of using Constitutional AI is pretty clear in how Claude acts. People often say it feels more cautious, more careful than other AI systems that might not have gone through such a strict, values-focused training. The constitution for Claude is a living document, too; it pulls from things like the Universal Declaration of Human Rights and rules from other big institutions. This helps ground its ethics in values most people agree on. The success of the Claude models is basically proof that this Constitutional AI thing isn’t just a research paper, it’s a real, working method for making safer AI. It shows we can build powerful tools that are way less likely to spit out toxic or biased junk, making them useful for business, schools, and everything else. These AI systems are a big step toward a future where we can actually work with AI safely.

• Principled “No”: Claude will actually explain why it refuses harmful requests, tying it back to its safety principles.
• Safe by Design: The whole thing is built around the constitutional AI training from the get-go.
• Less Toxic: The model is proven to be less likely to say offensive things.
• Aware of Bias: The training pushes the AI to recognize and try not to repeat bad societal biases.
• Tries to be Honest: The principles guide Claude to avoid just making stuff up and to admit when it doesn’t know something.
• The Constitution Can Change: The list of principles guiding Claude gets updated to deal with new problems and ethical issues.
• It Sells: The fact that these AI systems are successful proves that people are willing to pay for AI that is aligned with a constitution.
• Based on Human Rights: The constitution isn’t random; it includes principles from global standards like the UN Declaration of Human Rights.

The Core Framework: What Makes It Tick

To really get what is Constitutional AI, you have to look at its pieces. The method is a very systematic way to give AI systems a set of ethical rules. It’s a transparent, scalable way to do alignment that’s a huge leap forward for AI safety. The table below breaks down the key parts and goals that make up the Constitutional AI process, from writing the principles to training the final model. It shows how every step builds toward the main goal: an AI that’s helpful, harmless, and can think about its own actions.

This table is a rough outline of what is Constitutional AI, showing the principles and steps used to make safer AI systems. It all boils down to using a “constitution” to guide the AI, which means less reliance on direct human feedback for safety.\

Component / Phase	Description	Key Objective & Guiding Principles
The “Constitution”	Think of it like a rulebook for a robot brain. A really, really important rulebook. A set of explicit principles and rules written to guide the AI’s behavior and judgments.	To make the values of AI systems totally transparent and something you can edit. Principles are often pulled from big, serious sources like the UN Declaration of Human Rights.
Phase 1: Supervised Learning	This is the teaching phase. An initial AI model is prompted, then it critiques and rewrites its own responses based on the constitution.	The goal is to create a bunch of “good” examples, corrected by the AI itself. This dataset is then used for the first round of fine-tuning.
Phase 2: Reinforcement Learning	The scaling-up phase. A separate “preference” model is trained using AI-generated feedback, where an AI picks the better of two responses based on the constitution.	To train the final Constitutional AI model using Reinforcement Learning from AI Feedback (RLAIF). This is what makes the whole alignment process scalable.
Comparison to RLHF	It’s not the same as Reinforcement Learning from Human Feedback (RLHF), which is all about humans providing all the preference data for safety.	To build a system for alignment that’s faster, bigger, and hopefully less biased than having humans watch over everything all the time.
Core Goal	Basically, to train helpful and harmless AI assistants without needing tons of potentially problematic human labeling for every bad thing it might say.	To bake desired ethical behaviors right into the AI systems by using a predefined set of principles.
Example in Practice	Anthropic’s Claude models are the big, real-world examples of AI systems trained with this Constitutional AI method.	To make a commercially successful language model that is, by its very nature, helpful, honest, and harmless.

Source: Anthropic, “Constitutional AI: Harmlessness from AI Feedback” (2022)

As you can see, every piece of the Constitutional AI framework is there for a reason, to make the alignment of AI systems stronger and more transparent. The constitution is the ultimate source of truth, and the two-phase training process makes sure those principles get burned into the model’s behavior. It’s this structured way of doing things that really separates Constitutional AI from other, more informal safety methods.

Submission History: The Idea’s Evolution

The big reveal of Constitutional AI to the science world was when Anthropic published their paper back in December 2022. That paper was a big deal. It was a turning point in how people talked about AI safety. Before that, everyone was focused on RLHF, which was the standard but also had a lot of critics because of its scaling problems. Researchers were very interested in the paper because it offered a real, large-scale fix for one of the biggest problems in the quest for AGI. The idea of an AI supervising itself with a list of principles was new and powerful. It started a lot of conversations and new research projects about AI values.

Since then, the idea of Constitutional AI has been evolving and spreading. Anthropic is still the main company that has really built a product on this framework with its Claude models, but the ideas have infected the whole industry. Other big AI labs are looking into similar tricks for scalable oversight. The conversation isn’t about if we can automate alignment anymore, it’s about how. The “submission history” of this idea is more than just one paper now; it’s in talks at conferences, cited in other research, and it’s part of the strategy for companies building the next generation of AI systems. Transparency, scalability, explicit values—these are becoming goals for everyone, even if they don’t use the name “Constitutional AI“.

The Scalability Part is a Huge Deal

One of the best things about the Constitutional AI approach is just how huge it can scale. It’s incredibly efficient. Making these big AI systems takes a crazy amount of data and computer power; trying to align them with human feedback (RLHF) takes a crazy amount of human labor. As the models get bigger, the number of people you’d need to guide them would just explode. It’s a huge bottleneck. Constitutional AI breaks that chain. By using an AI to generate the safety labels, the process can scale as big as your computing resources allow. An AI can check millions of answers in the time it takes a human team to do a few thousand.

And it’s not just about being faster or cheaper. It’s about quality. Humans get tired, they’re inconsistent, they have biases. An AI preference model, working from a clear list of principles, can apply the rules the exact same way, every single time, over millions of examples. So you get a more uniformly aligned model. And, if a new problem pops up, you don’t have to hire and train a whole new team of people. You just add a new principle to the constitution and run the training again. That agility makes AI systems trained this way not only safer when they launch but also way easier to fix and improve later.

• Scales with Computers: It scales with processing power, not people. That’s efficient.
• Saves Money: It really cuts down the cost of massive human annotation projects for AI systems.
• Faster Turnaround: Updating the constitution and retraining the AI is way quicker than getting new human feedback.
• Consistent Rules: An AI applies the principles without the randomness of human judgment.
• Good for Giant Models: This approach is much better for aligning the huge models of the future.
• Makes Its Own Data: The AI generates its own preference data, which is like a self-powering alignment loop.
• Quick on Threats: New safety principles can be added on the fly to deal with new risks.
• Breaks the People Bottleneck: Constitutional AI might be how we align superintelligent AI when human oversight is just too slow to keep up.

A Policy Memo on Ethical AI

For any organization, whether it’s a tiny startup or a huge government agency, using AI systems is a double-edged sword. There’s huge potential, but also huge risk. Adopting something like Constitutional AI isn’t just a tech decision; it’s a strategic policy move. It shows you’re serious about ethical development, managing risk, and building trust. The idea is to stop being reactive, just filtering bad stuff, and start being proactive by building AI that is safe from the ground up. A policy built on these principles would mean that any AI model you use has gone through a transparent alignment process that people can check. The constitution itself becomes a public document, showing your commitment to things like privacy, fairness, and not spreading misinformation.

Making this policy happen takes a few steps. First, you have to actually write or adopt a constitution that fits your organization’s values and the laws of where the AI will be used. You should probably get ethicists, lawyers, and engineers in a room for that. Second, you need to invest in the tech and the people who know how to do the RLAIF training. Third, you have to commit to watching over it. The constitution can’t be set in stone; it has to be a living document that gets reviewed and updated. By making what is constitutional AI a key part of your AI strategy, you can show the world you’re serious about safety, which makes your AI systems more trustworthy to everyone.

Challenges and What’s Next

Okay, so Constitutional AI isn’t a silver bullet for every alignment problem. A big criticism is that everything depends on how good the constitution is. If the founding principles are bad, or biased, or just don’t cover everything, then you’re just systematically building those flaws into the AI. Trying to write a “good” constitution that works for everyone is a massive philosophical and cultural mess. Whose values do you use? What happens when two principles conflict? They call this the “value-loading problem,” and people are still trying to figure it out. There’s also the risk of “reward hacking”, where the AI figures out how to follow the exact words of the constitution while totally ignoring its spirit, leading to weird results.

The future of Constitutional AI is all about tackling these problems. Researchers are working on better ways to create and test constitutions, maybe even asking the public for input or using fancy tech to balance conflicting principles. Another big thing is making the AI better at the “critiquing” part in that first supervised phase. If the AI can give itself smarter critiques, the training data gets better and the final model will be more deeply aligned.

Improving this self-critique mechanism is a sophisticated task that falls under the umbrella of content engineering, which focuses on structuring interactions to produce reliable AI outputs. For instance, developers could use chain of thought prompting during the supervised phase, forcing the AI to explicitly reason step-by-step why its initial response violates a specific constitutional principle before rewriting it. Similarly, decomposed prompting could be used to break the complex task of “critique and rewrite” into smaller, more manageable sub-tasks, such as ‘first, identify any biased language; second, suggest a neutral alternative; third, rewrite the full response incorporating the change.’ By applying these more advanced prompting strategies, the self-correction loop at the heart of Constitutional AI becomes more rigorous and effective.

The end goal is to have a flexible system where the AI systems and their guiding principles can grow together, getting safer and more useful over time. We’re still at the beginning of this journey.

• The Value-Loading Problem: It is incredibly hard to define a set of principles that is universally good and free of bias.
• Reward Hacking is a risk: The AI could find loopholes to follow the rules in unintended, unhelpful ways.
• Is the Constitution Complete?: A constitution might not cover every weird situation, leaving holes in the AI’s ethics.
• Conflicting Principles: What happens when two principles tell the AI to do opposite things? The system needs a way to decide.
• Quality of AI Critiques: The whole process hinges on the AI being good at giving itself high-quality feedback.
• Maintaining the Thing: Keeping the principles up-to-date with society is a never-ending job.
• Transparency vs. Complexity: The more complex a constitution gets to cover all the edge cases, the less transparent and easy to understand it becomes.
• Beyond Language Models: Figuring out how to apply Constitutional AI to other kinds of AI systems, not just language ones, is a future challenge.

The Broader Impact on Society

The invention of Constitutional AI matters way beyond the tech world. It’s a real step towards a future where we can build and use advanced AI systems and feel more confident that they won’t go haywire. For society, that could mean more responsible AI in really important fields like medicine, finance, and law. An AI that’s guided by a solid, rights-respecting constitution is just less likely to be discriminatory or give dangerous advice. This is huge for building public trust; when developers can show you the literal principles an AI is following, it makes its behavior less mysterious and creates accountability.

Also, the whole “constitution for an AI” idea is a great way to frame conversations about AI rules and regulations around the world. It gets governments thinking not just about banning things, but about defining the positive values we want our technology to have. As we get closer to AGI, having scalable and transparent alignment methods like Constitutional AI is going to be absolutely essential. It’s a way to make sure these future superintelligent AI systems aren’t just black boxes with weird motives, but are aligned partners whose operating principles we can see and control.

Common Questions

See common questions about this topic.