Anthropic trained the model behind Claude using a written rulebook instead of relying only on human raters. Here's what that actually means, in plain English, and what it doesn't mean.
Constitutional AI is a training method Anthropic published in 2022 that uses a written set of principles, not just human ratings, to shape how a model responds, especially to harmful or borderline requests. It cuts down how much a human reviewer has to rate directly, and it makes the standard something you can actually read instead of an implicit pattern buried in thousands of ratings. It’s one input into a model’s behavior, not a promise the model will never make a mistake. Whichever AI tools your company uses, the real value here is a sharper question to ask any vendor: not just whether their tool is safe, but how it was actually trained to be, and whether they can show you.
Say you’re an HR operations lead piloting a new AI assistant for employee questions about benefits and leave policy. The vendor’s sales deck has a line saying the model is “trained to be safe and helpful.” You ask what that actually means, expecting a sentence or two back. What you get is a shrug and a link to a trust page that mostly talks about data encryption. Nobody in the room can actually tell you what “safe” was built out of.
That gap, between a company saying its AI was trained well and being able to explain how, is what this piece is about. Not because you need to understand machine learning to use these tools responsibly. You don’t. But there’s one specific idea from Anthropic, the company that builds Claude, called Constitutional AI, that’s worth understanding in plain English, because it changes what a “safety” claim from any AI vendor can actually mean.
Here’s the problem it was built to solve. For years, the main way a language model learned which responses were better than others was human feedback: people compared two draft answers side by side and picked the one that was more helpful or less harmful, over and over, at enormous scale. Anthropic has described this process directly: human contractors compared two responses from a model and selected the one they felt was better according to some principle, like being more helpful or more harmless.[1] That approach works. It’s also expensive, slow to scale, and it means the people doing the rating are the ones absorbing whatever genuinely disturbing content the model produces along the way.
As models got more capable and started producing longer, more complex responses, that process started to strain. Anthropic’s own account of the shortcomings is specific: crowdworkers reviewing a growing volume of responses find it harder to keep up with or fully understand increasingly complex outputs, and reviewing even a subset of them takes real time and money that many researchers simply don’t have.[1] Anthropic wanted a way to keep shaping model behavior at scale without needing a human to look at, and be exposed to, every harmful response along the way.
In December 2022, Anthropic published a paper describing an alternative called Constitutional AI.[2] Stripped of the machine-learning terminology, the idea is this: instead of relying only on humans to rate whether each response is harmful, give the model a written set of principles, call it a constitution, and have the model use those principles to critique and improve its own draft answers.
Picture a junior writer handed a style guide instead of having a supervisor read every single draft. The style guide won’t catch everything a sharp editor would, but it lets the writer correct a lot of their own work before anyone else has to look at it, and the standard is written down somewhere anyone can actually read.
Anthropic’s own description of the process: the constitution gets used at two points in training. In the first phase, the model is trained to critique and revise its own responses using the written principles and a handful of examples. In the second phase, the model is trained with reinforcement learning, but instead of a human choosing the better of two responses, a model does the choosing, based on how well each response follows the principles.[1] Anthropic calls this second part “RL from AI Feedback,” or RLAIF, because AI supervision is doing a job a human reviewer used to do.
A simplified version of the two-phase process Anthropic describes in “Claude’s Constitution” and the original Constitutional AI paper. Source: Anthropic, “Claude’s Constitution,” May 2023, and “Constitutional AI: Harmlessness from AI feedback,” Dec 2022. [1][2]
If nobody can point to where a model’s values came from, you can’t evaluate whether they’re the right values for your business.
The point Anthropic makes about why this matters has more to do with transparency than efficiency, even though the process does cut down how much direct human review is needed. A written constitution can be read, inspected, and argued with. A value that only exists as an emergent pattern across millions of anonymous human ratings can’t be pointed to in the same way.[1] That’s a genuinely different kind of claim than “we trained it to be safe,” and it’s worth being able to tell the two apart.
Constitutional AI isn’t a replacement for human-feedback training so much as a specific fix applied to one part of it. The broader technique it sits on top of is called reinforcement learning from human feedback, or RLHF: a model generates responses, a human rates or compares them, and that feedback trains a reward model that shapes future responses. Most major AI labs have used some version of this to make their models more helpful and less likely to produce harmful output.
What changes with Constitutional AI is specifically the harmlessness side of that process. Anthropic’s own account is direct about it: their Constitutional-AI-trained model received no human data on harmlessness at all, meaning every result on harmlessness came purely from AI supervision checked against the written principles.[1] Helpfulness training still draws on human feedback in Anthropic’s approach. The shift is narrower than “AI trains itself now,” and it’s worth resisting the broader claim, because it oversells what actually changed.
| Standard RLHF (harmlessness) | Constitutional AI | |
|---|---|---|
| Who judges each response | A human reviewer compares two draft responses | A model compares two draft responses, checked against written principles |
| Where the standard lives | Implicit in thousands of individual human judgment calls | Written down as a specific set of principles anyone can read |
| What reviewers are exposed to | Large volumes of harmful or disturbing draft content, directly | Far less: AI feedback absorbs most of the harmful-content review |
| How it scales | Limited by how many human hours can be spent reviewing | Scales with compute rather than reviewer headcount |
Simplified from Anthropic’s own description of Constitutional AI training. Source: Anthropic, “Claude’s Constitution” and “Constitutional AI: Harmlessness from AI feedback.” [1][2]
There’s a practical difference this produces that Anthropic specifically tested for: a model trained this way can respond to a hostile or adversarial question by explaining its objection rather than just refusing outright or dodging the question entirely. Anthropic describes the result as a model that’s harmless but “non-evasive,” able to engage with a difficult question and say why it won’t go further, instead of shutting the conversation down.[2] That’s a design choice, not an automatic outcome of any safety training. A model can be trained to be safe by refusing anything that looks risky, which also makes it frustrating to use, or it can be trained to explain itself. In Anthropic’s own testing, Constitutional AI training was associated with the second.
None of this is only relevant if you work in AI research. Say you’re a marketer testing two different AI writing assistants against the same campaign brief. One flatly refuses a request that touches a sensitive health claim, no explanation, just a canned “I can’t help with that.” The other explains specifically what it’s cautious about and offers a version that stays inside the lines. That difference in behavior isn’t random. It traces back, at least in part, to choices made during training about how the model should weigh being helpful against being cautious, and whether “safe” means blocking or explaining.
This plays out differently depending on the job. What “trained to be safe” should actually mean changes with the role:
If your company has started using an agentic tool like Claude Cowork, one that takes real actions rather than only producing text, this question gets more important, not less. The stakes of a wrong judgment call go up once a model isn’t just talking, it’s doing something on your behalf.
Anthropic’s most recent version of Claude’s constitution, published in January 2026, makes its priority ordering explicit for anyone who wants to check it: the model is meant to weigh being broadly safe first, then broadly ethical, then compliant with Anthropic’s own guidelines, then genuinely helpful, in that order when those things come into conflict.[4] Whether or not you ever read that document yourself, it’s a useful example of what “trained with a written approach” can actually look like: specific priorities, in a specific order, that someone outside the company can read and argue with.
Worth saying plainly: this doesn’t mean every AI vendor works this way, and it doesn’t mean you should assume a tool is well-behaved just because a company you’ve heard of built it. It means that when a vendor makes a claim about how their model was trained to behave, there’s now a real, checkable difference between “we trained it to be safe” as a slogan and “here’s the written approach, here’s what it prioritizes, here’s where you can read it” as an actual answer.
Here’s where it’s worth being precise, because it’s easy to hear “trained using a written constitution” and assume that means a model can’t misbehave. Anthropic doesn’t claim that, and neither should anyone summarizing their work. Anthropic’s own description of the original approach calls it “not a perfect approach,” and the January 2026 constitution is described as a living document whose outputs “might not always adhere to the constitution’s ideals.”[1][4]
That’s worth sitting with, because it’s the most important thing to take from this whole topic, not a footnote to skip past. A training method changes the odds that a model behaves the way its creators intended. It doesn’t remove the need to check what it actually produced.
A training method changes the odds a model behaves well. It doesn’t remove your job of checking the output.
| What it does | What it does not guarantee |
|---|---|
| Gives a written, inspectable set of principles the model is trained against, instead of an implicit standard buried in ratings | That the model follows those principles in every response, every time |
| Reduces, though doesn’t eliminate, how much harmful content human reviewers have to look at directly during training | That the model is free of bias, factual errors, or bad judgment on edge cases |
| Lets a model explain its reasoning rather than simply refusing outright, when it declines a request | That every company building on the model applies the same standard, or any standard, at deployment |
| Gives you something specific to read and question, where the constitution is published | That reading the constitution tells you exactly how the deployed product behaves in your specific use case |
Based on Anthropic’s own stated limitations for Constitutional AI and Claude’s constitution. Sources: [1][4]
This is also where it helps to separate two different things people mean by “safe AI tool.” One is the training approach: what shaped the model’s underlying behavior. The other is deployment-level controls: content filters, human review queues, access restrictions, audit logs, the layer a company adds on top of a model for its own use case. Constitutional AI sits entirely in the first category. A tool can be built on a thoughtfully trained model and still be poorly deployed, with no review process and nobody checking what it actually says to your customers. The training story and the deployment story are both real, and they’re not the same story.
Future Factors trains professionals across whichever AI tools their company already has, not just one. That’s the honest lens for this last part: understanding Constitutional AI is useful less because Claude uses it and more because it’s a concrete example of what a real answer to “how was this trained?” can look like, so you can ask any AI vendor a sharper question and recognize a non-answer when you hear one. If you’re actively comparing tools, our breakdown of ChatGPT vs Claude vs Gemini for work covers more of the practical differences beyond training approach.
A short, practical checklist for when your company is evaluating an AI tool, whichever one it is:
| Vendor says | What to ask next |
|---|---|
| “Our AI is trained to be safe.” | Trained how, specifically? Is there a published document, paper, or policy you can point me to? |
| “We use industry-standard safety training.” | Which standard? Human review, a written policy, both? Whose review, and how often? |
| “The model has strong guardrails.” | Are those guardrails part of the model’s training, or a filter your company added on top? Those fail differently. |
| “It’s been tested extensively.” | Tested against what, by whom, and is any of that testing published anywhere you can check? |
A practical companion to the questions above, for a procurement or IT conversation about a new AI tool.
Ask how a model was shaped before you ask whether it’s safe. “Safe” isn’t one measurable thing.
None of this replaces your own review process. If anything, understanding how training actually works is a good argument for keeping one: even a well-documented, thoughtfully trained model is still a model, producing output a person on your team should be checking before it reaches a customer, a regulator, or an employee’s paycheck. This is the same gap, between what a vendor claims and what your company can actually verify, that shows up as AI tools get embedded deeper into daily work, which we cover more broadly in our piece on the AI governance gap.
Next time a vendor’s deck says their AI was “trained to be safe,” you now have a specific, real example to compare it against, and a better question to ask than the one that gets you a shrug.
Constitutional AI is a training method Anthropic published in December 2022 that uses a written set of principles, a “constitution,” to guide a model’s own self-critique and revisions during training, instead of relying only on humans to rate whether each response is harmful. It’s one input into how a model behaves, not a guarantee of good behavior in every response.
Anthropic, the AI safety and research company that also builds Claude, published the original Constitutional AI research in December 2022 and has since built the idea into a broader constitution that guides Claude’s training, most recently updated in January 2026. Other AI labs use their own named approaches for a similar purpose, like OpenAI’s published model spec, which aims to make a model’s intended behavior explicit and checkable.
Standard human-feedback training (RLHF) relies on people comparing pairs of model responses and picking the better one, which is slow to scale and exposes reviewers to harmful content directly. Constitutional AI replaces most of that harmlessness review with AI feedback checked against written principles, while helpfulness training still draws on human feedback. The standard becomes something written down and inspectable, not just an implicit pattern across thousands of ratings.
No. Anthropic is explicit that this isn’t a perfect approach, and that a model’s actual output won’t always match its written principles. A training method changes the odds a model behaves as intended. It doesn’t remove the need for a person to review what the model actually produces, especially for anything reaching a customer, employee, or regulator.
Because it gives you a concrete standard to hold any AI vendor to. When a company says its tool is “trained to be safe,” you can now ask what that specifically involved: a published set of principles, human review, both, or neither. That question, more than the label “safe,” is what actually tells you something useful before you put a tool in front of your team or your customers.
Every claim about how Constitutional AI works, what it does, and what it doesn’t guarantee was checked directly against Anthropic’s own live pages on 2 September 2026, not recalled from training data: the original Constitutional AI research page (published Dec 15, 2022), the underlying arXiv paper (2212.08073), “Claude’s Constitution” (published May 9, 2023, updated Jan 21, 2026), and “Claude’s new constitution” (published Jan 22, 2026). The one claim in this piece not about Anthropic, that OpenAI publishes a comparable “model spec,” is sourced to Anthropic’s own footnote on its January 2026 constitution post, not independently verified against OpenAI’s own site, and is flagged here rather than presented as separately confirmed. This piece deliberately does not compare how well different AI vendors execute on safety claims. It explains one company’s published method well enough that you can ask better questions of any vendor, including ones that publish nothing at all.
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