Constitutional AI
Constitutional AI (CAI) is a training approach developed by Anthropic in which an AI model orients itself by a set of guiding principles called a "constitution". These principles help the model give helpful, honest and safe answers. Unlike purely human-guided feedback, the model assesses and corrects many of its answers itself against the defined principles.
Also known as: CAI, constitutional AI, constitutional AI training
What does constitutional AI mean?
Constitutional AI describes a method for aligning large language models' behaviour with clearly stated principles. Together those principles form a so-called constitution setting how a model should answer: helpful to the person asking, honest in what it says and safe in the sense of avoiding harmful content.
The approach was developed by the AI company Anthropic developed, which uses it among other things in Claudemodel family. The basic idea is not only to show the model many individual examples of good behaviour but to give it overarching guidelines against which it can check and improve its own output. The constitution is not a technical filter blocking particular words but a collection of clearly stated principles the model takes into account in weighing its answers.
Constitutional AI is therefore an important building block in AI security and of what is called AI alignment, that is, aligning AI systems with human values and expectations.
How does constitutional AI work?
The training process can be divided conceptually into two phases. In the first, the model produces answers and is then guided to criticise and revise them itself against the constitutional principles. That creates a dataset of improved answers on which the model is trained further.
In the second phase, which builds on reinforcement learning, the model compares different possible answers and uses the principles to choose the one fitting the constitution better. Those preference decisions feed back into further training. Because the assessment here is made by the AI system itself on the basis of the principles, this is also called Reinforcement learning from AI feedback.
The decisive difference from classic approaches is that a considerable part of the evaluation is done by the model itself on the basis of the principles, rather than solely through manually produced human ratings. The human oversight thus shifts from judging individual answers to shaping the underlying rules.
Constitutional AI compared with RLHF
A common method for aligning language models is Reinforcement learning from human feedback (RLHF), in which people rate many model answers and the model learns from those ratings. The approach is effective but demanding, since it requires a great many human judgements. The criteria can also stay implicit, because they are not always written down explicitly.
Constitutional AI complements and extends this idea by basing part of the feedback on the constitution's principles. The model can thus make many of the assessments itself, which can make the process more transparent and more scalable. The principles are stated explicitly and can be followed, so it is easier to check which values the model's behaviour is meant to follow.
The two approaches do not exclude each other. In practice different methods are often combined to make models as helpful, honest and safe as possible. Constitutional AI is therefore less a replacement for RLHF than a development of it, joining human oversight and machine self-assessment.
Examples of constitutional principles
The principles of an AI constitution are generally phrased in natural language and kept general, so they apply to many different situations. One principle might require the model to prefer answers that are honest and contain no misleading claims. Another can aim at the model staying respectful and producing no content that could demean or endanger people.
Such principles often have to be weighed against each other: an answer should be as helpful as possible but at the same time must not breach safety principles. Because the principles are written out, they can also be discussed, revised and adapted to changing requirements.
Limits and open questions
Constitutional AI is a promising approach but no complete guarantee of safe behaviour. Its effectiveness depends among other things on how well the principles are phrased and how reliably the model applies them in different situations. Principles can be incomplete, contradict one another or be interpreted unexpectedly in rare edge cases.
The question of who sets the principles, and by what standards, matters too. Values are not the same in every context, and a constitution necessarily reflects particular decisions. Careful, transparent design and continuing review are therefore important, which is why human oversight remains a central part of it.
Despite these open questions, constitutional AI shows that safety considerations can be built explicitly into the training of language models.
Why does constitutional AI matter?
As AI systems are used more widely, safety, traceability and responsible behaviour matter more. Constitutional AI addresses exactly these requirements by making the guidelines for model behaviour explicit.
For organisations, a model aligned to clear principles can be a building block of a well-thought-out AI governance . When it is clear which values a system follows, its use is easier to judge and to embed in existing policies.
At Elisabit we help companies AI solutions responsibly and choose them to fit their requirements. Understanding approaches such as constitutional AI helps assess the properties of modern language models and find suitable tools for each purpose.
Frequently asked questions
Who developed constitutional AI?
Constitutional AI was developed by the AI company Anthropic. The approach is used in the Claude model family among others and aims to make models helpful, honest and safe.
What is the constitution in constitutional AI?
The constitution is a set of guiding principles the model follows. These principles describe in natural language how the model should answer and serve as the basis for it to check and improve its own output.
How does constitutional AI differ from RLHF?
In RLHF, people do most of the rating of model answers. Constitutional AI bases part of the feedback on the explicitly worded principles of the constitution, so the model can make many judgements itself. The two approaches are often combined in practice.
Does constitutional AI guarantee safe behaviour?
No, constitutional AI is an effective approach but not a complete guarantee. The quality depends on how well the principles are worded and how reliably the model applies them.
Why is constitutional AI interesting for companies?
A model aligned to clear principles can be a building block of well-considered AI governance. When it is clear which values a system follows, its use is easier to judge and to embed in existing policies.
Related terms
Claude is Anthropic's family of language models, comprising Haiku, Sonnet, Opus and the new flagship model Fable 5.
An AI company focused on safety, known for the Claude models and for MCP.
AI alignment means bringing the goals and behaviour of AI systems into line with human values and safety.
RLHF is a training method that uses human feedback to make model responses more helpful and safer.
An LLM is an AI language model that understands and produces text by predicting the most likely next word.
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