constitutional-ai_skill

Overview

This skill implements Constitutional AI, a two-phase method that trains models to be harmless using self-critique and AI-generated feedback instead of human labels. It combines a supervised self-revision stage with RLAIF (reinforcement learning from AI feedback) to align models to a user-defined constitution of principles. The approach is practical for producing explainable, less evasive, and safer model behavior at scale.

How this skill works

During the supervised phase the model generates initial answers, generates critiques against a constitution of principles, and revises its responses; those revised pairs are used to fine-tune the model. In the RL phase the system samples multiple responses per prompt, uses AI evaluators to compare responses according to the constitution and trains a reward model from those preferences, then applies PPO-style training with that reward model. Chain-of-thought critiques and majority voting across multiple evaluators reduce noisy preferences.

When to use it

• You need safety alignment without expensive human preference labels
• You want explainable safety decisions and transparent critiques
• You aim to reduce evasive refusals while still avoiding harmful outputs
• You have a clear set of principles or domain-specific policy to enforce
• You need a scalable pipeline for safety training across large datasets

Best practices

• Write a concise, prioritized constitution that balances helpfulness and harmlessness
• Use multiple rounds of critique+revision when initial revisions are weak
• Use multiple AI evaluators and majority voting to reduce noisy RLAIF preferences
• Include chain-of-thought critique prompts to surface reasoning and improve revisions
• Monitor for over-refusal and add constitution principles that favor thoughtful engagement

Example use cases

• Train a customer-service assistant that explains safety concerns instead of bluntly refusing
• Align a research model to avoid toxic, biased, or privacy-violating outputs without human labels
• Build a moderation-adjacent reward model via RLAIF to rank safer responses
• Prototype safety policies quickly by iterating on constitutions and critique prompts
• Combine with RLHF later when human-labeled preferences become available for fine-grained control