Constitutional AI
Definition
Constitutional AI is an alignment technique developed by Anthropic that trains models to follow a set of principles (a 'constitution') using self-critique and revision, reducing reliance on human feedback for safety training.
Why It Matters
Constitutional AI represents a different approach to AI safety than pure human feedback. Instead of labeling thousands of examples as harmful or harmless, you specify principles the model should follow. The model then critiques and improves its own outputs based on these principles.
The practical benefit: scalability. Collecting human safety labels is expensive and inconsistent. Different labelers disagree on edge cases. Constitutional AI generates its own training signal by asking “does this response violate principle X?” and revising accordingly.
For AI engineers, Constitutional AI explains why Claude behaves differently than other models. Its training includes explicit principles about helpfulness, harmlessness, and honesty. Understanding these principles helps you work with the model’s tendencies rather than fighting them.
Implementation Basics
Constitutional AI has two training phases that work together:
1. Supervised Constitutional Phase (Red Teaming + Revision) Generate harmful outputs using red-team prompts. The model then critiques these outputs against constitutional principles and revises them to be harmless. Train on (harmful_prompt, revised_helpful_response) pairs.
Example principle: “Choose the response that is most supportive and encouraging of life, liberty, and personal security.”
2. RL from AI Feedback (RLAIF) Instead of human preference labels, the model itself evaluates which responses better follow the constitution. Use these AI-generated preferences to train a reward model, then do reinforcement learning as in RLHF.
Constitutional principles typically cover:
Helpfulness - Respond to legitimate requests, be informative, acknowledge uncertainty.
Harmlessness - Refuse to help with illegal or dangerous activities, don’t generate harmful content, be honest about capabilities.
Honesty - Don’t pretend to be human, acknowledge being an AI, don’t make up information.
Practical implications for users:
Predictable Boundaries - Constitutional models have principled refusals rather than arbitrary ones. Understanding the constitution helps predict what the model will and won’t do.
Self-Consistency - Constitutional training aims for consistent behavior. The model should refuse similar requests similarly, making behavior more predictable.
Reasoning Transparency - Models can explain their reasoning with respect to principles, making refusals more understandable and less frustrating.
For application development, Constitutional AI models typically require less prompt engineering around safety, since the model handles guardrails internally. Focus your prompts on the task rather than on preventing harmful outputs.
Source
Constitutional AI uses a set of principles to guide model self-improvement through critique and revision, then trains on the self-generated preference data, achieving safety improvements without human harm labels.
https://arxiv.org/abs/2212.08073