Artificial Intelligence

What Is Constitutional AI and How Does It Try to Make Models Safer?

By Noah BennettPublished: June 16, 2026

Getting an AI to behave well is harder than it sounds. You can't just tell a language model to "be helpful and not harmful" and expect it to figure out the details. The model needs something more concrete – a framework, a set of principles, a way of evaluating its own outputs against a standard it actually understands.

That's the problem Constitutional AI was designed to solve. It's an approach to AI alignment developed by Anthropic, and it's one of the more interesting attempts to make AI systems safer without relying entirely on humans to review every single thing the model produces.

The Problem It's Trying to Fix

Before Constitutional AI, the dominant method for making language models behave appropriately was a technique called Reinforcement Learning from Human Feedback, or RLHF. The basic idea: show human reviewers pairs of AI responses, ask them which one is better, and use those preferences to train the model over time. The model gradually learns to produce outputs that humans rate positively.

RLHF works reasonably well, but it has a few notable weaknesses. It's expensive and time-consuming, because you need a large workforce of human reviewers. It's also inconsistent – different reviewers have different standards, different cultural assumptions, and different tolerances for what counts as harmful. And crucially, the model has no explicit understanding of why certain responses are preferred. It learns patterns from feedback, but it can't reason about the principles behind those patterns.

Constitutional AI, introduced by Anthropic in a 2022 research paper, tries to address these limitations by giving the model an actual set of written principles – a "constitution" – and training it to evaluate and revise its own outputs against those principles.

What the "Constitution" Actually Is

The constitution in Constitutional AI is a list of principles written in plain language. Anthropic's original version drew from a range of sources: the UN Declaration of Human Rights, Apple's terms of service, various research papers on AI safety, and principles developed internally by the Anthropic team.

The principles cover things like: avoid producing content that could be used to harm people; don't assist with activities that are deceptive, harmful, or dishonest; try to be genuinely helpful rather than just compliant; protect user privacy; don't take actions that concentrate power inappropriately. They're written as values, not as a list of banned topics – which turns out to matter quite a bit for how the training process works.

The specific principles can be adjusted, which is part of the point. Different applications or contexts might call for different constitutions. The framework is flexible; the particular values encoded in any given version are a deliberate design choice.

How the Training Process Works

Constitutional AI uses a two-phase training process, and the mechanics of both phases are genuinely interesting.

Phase one: Supervised learning with self-critique. In the first phase, the model is prompted to generate responses to a wide variety of questions and requests – including deliberately difficult or sensitive ones. Then, crucially, the model is asked to critique its own responses based on the constitutional principles. It's essentially asked: "Does this response violate any of these principles? How could it be revised to better align with them?" The model then generates a revised version of its response. This process – generate, critique, revise – produces a dataset of improved responses that is used to fine-tune the model.

This is the part that makes Constitutional AI structurally different from standard RLHF. Instead of relying on human reviewers to evaluate responses, the model is doing a significant portion of the evaluation itself, guided by the written principles.

Phase two: AI feedback for reinforcement learning. The second phase uses a technique Anthropic calls RLAIF – Reinforcement Learning from AI Feedback. Rather than having human raters compare pairs of responses, a separate AI model does the comparison, again guided by the constitutional principles. This AI evaluator scores which response better aligns with the principles, and those scores are used to train a reward model, which is then used to further fine-tune the original model through reinforcement learning.

The result is a model that has been trained not just on human preferences, but on explicit, articulable principles – and that has been shaped through a process involving its own reasoning about those principles.

Why This Approach Is Interesting

A few things make Constitutional AI worth paying attention to beyond the technical details.

The first is transparency. Because the principles are written out in plain language, they can be read, critiqued, and debated. That's different from the implicit preferences encoded in a dataset of human feedback, which are essentially invisible. When Anthropic publishes its constitution, anyone can look at the values it encodes and evaluate whether they agree with those choices. That's a meaningful form of accountability.

The second is scalability. Human review doesn't scale easily – there are only so many human reviewers available, and the cost of reviewing billions of model outputs is prohibitive. AI-assisted feedback, guided by written principles, can operate at a much larger scale. This matters a lot as AI systems become more capable and their outputs become more consequential.

The third is reasoning about edge cases. One of the hardest problems in AI safety is handling situations that weren't explicitly anticipated during training. A model trained purely on human feedback has no principled way to reason about novel situations – it can only pattern-match to things it's seen before. A model trained with Constitutional AI has something closer to a framework it can apply to new situations, because the principles are generative rather than just case-specific.

The Honest Limitations

Constitutional AI is genuinely promising, but it's not a complete solution to AI safety – and Anthropic has been fairly upfront about that.

The principles themselves reflect the values and assumptions of the people who wrote them. That means Constitutional AI doesn't eliminate human bias from the training process; it concentrates it in a more legible place. This is actually arguably better than hiding it in unlabeled human feedback, but it's still a real consideration. Whose values get encoded in the constitution? Who decides what counts as "harmful"? These are legitimately hard questions.

There's also the question of whether the model's self-critique is reliable. When a model evaluates its own outputs against a set of principles, it's using the same underlying system that generated those outputs in the first place. If the model has blind spots or systematic errors, those may persist through the self-critique process. The principles provide guidance, but they can't guarantee the evaluation is accurate.

And like all current approaches to AI alignment, Constitutional AI operates on the model at training time – it shapes the model's dispositions but can't fully control its behavior in every possible deployment context. A model can be trained to generally avoid certain behaviors while still producing them in unusual or adversarially designed situations.

What It Means in Practice

If you've used Claude – the AI assistant built by Anthropic – you've interacted with a model trained using Constitutional AI. The approach is designed to produce a model that's not just compliant in a mechanical way, but that has something closer to an internalized understanding of why certain responses are better than others.

The practical effect, in theory, is a model that can handle genuinely complex situations more thoughtfully – where the right response isn't obvious and requires weighing competing considerations. Rather than a model that pattern-matches to "safe" outputs, the goal is a model that reasons about what a helpful, honest, and harmless response actually looks like in context.

Whether that goal is fully achieved in practice is an ongoing empirical question. The field of AI alignment is young, the evaluation methods are imperfect, and even the researchers who developed Constitutional AI would be the first to say there's a lot still to figure out.

The Bigger Picture

Constitutional AI is part of a broader effort in the AI industry to move from alignment-by-instinct to alignment-by-design. The recognition driving this work is that as AI systems become more capable, "we'll just review the outputs" stops being a viable safety strategy. You need systems that have internalized something about what they should and shouldn't do – and you need that internalization to be legible enough to evaluate, critique, and improve.

That's a genuinely hard problem, and no one has fully solved it. But the Constitutional AI approach represents a substantive attempt to make the values embedded in AI training explicit, debatable, and scalable. In a field where those properties are rare, that matters.

FAQ

Is Constitutional AI unique to Anthropic? The specific technique was developed and named by Anthropic, though other labs are exploring related ideas around principle-based training and AI feedback. The 2022 paper "Constitutional AI: Harmlessness from AI Feedback" laid out the methodology in detail and remains the primary reference.

Does Constitutional AI make a model perfectly safe? No – and Anthropic doesn't claim that it does. It's one component of a broader safety approach. No current alignment technique produces a model that behaves appropriately in all possible situations, and Constitutional AI is no exception. It's a meaningful improvement over earlier approaches, not a final answer.

Can the constitution be changed? Yes. The specific principles in any given version of a constitutional AI training process are design choices, not fixed features of the technique. Different versions, applications, or organizations could in principle use different constitutions – which raises its own set of questions about whose values get encoded and how those choices are made.

What is RLAIF and how is it different from RLHF? RLHF (Reinforcement Learning from Human Feedback) uses human raters to evaluate model outputs. RLAIF (Reinforcement Learning from AI Feedback) uses a separate AI model to do the evaluation, guided by explicit principles. RLAIF is faster and more scalable, but it shifts the trust question: instead of trusting human raters, you're trusting that the AI evaluator is applying the principles accurately.

Where can I read the original research? Anthropic published the Constitutional AI paper in December 2022. It's available on arXiv at https://arxiv.org/abs/2212.08073 and is worth reading if you want to go deeper into the technical methodology.