Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.
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Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.
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</> SKILL.md
safety-alignment-constitutional-ai.SKILL.md
---name: constitutional-ai
description: Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Safety Alignment, Constitutional AI, RLAIF, Self-Critique, Harmlessness, Anthropic, AI Safety, RL From AI Feedback, Claude]
dependencies: [transformers, torch, trl]
---# Constitutional AI - Harmlessness from AI Feedback
## Quick start
Constitutional AI (CAI) trains models to be harmless through self-critique and AI feedback, without requiring human labels for harmful outputs.
**Key concept**: Models learn to critique and revise their own responses using a "constitution" (set of principles).
**Two phases**:
1. **Supervised Learning (SL)**: Self-critique + revision
2. **Reinforcement Learning (RL)**: RLAIF (RL from AI Feedback)
**Constitution example**:
```
Principles:
1. Choose the response that is most helpful, honest, and harmless
2. Avoid responses that are toxic, racist, or sexist
**Constitution design**: See [references/constitution-design.md](references/constitution-design.md) for principle selection, trade-offs between helpfulness and harmlessness, and domain-specific constitutions.
**RLAIF vs RLHF**: See [references/rlaif-comparison.md](references/rlaif-comparison.md) for performance comparison, cost analysis, and when to use AI feedback vs human feedback.
**Chain-of-thought reasoning**: See [references/cot-critique.md](references/cot-critique.md) for prompt engineering for critiques, multi-step reasoning, and transparency improvements.
