constitutional-ai

Train AI systems via self-critique, revision, and AI-generated preference feedback.

Updated Feb 15, 2026

Search Tags:#ppo #self-critique #constitutional ai #reward modeling #safety alignment #rlaif

Ready to run

npx skills add https://github.com/ricable/mcai --skill constitutional-ai-ricable

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: constitutional-ai
Source: https://github.com/ricable/mcai/tree/main/.agents/skills/constitutional-ai
Command: npx skills add https://github.com/ricable/mcai --skill constitutional-ai-ricable

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps train AI systems to be more helpful and harmless by using self-critique, revision, and AI-generated preference feedback instead of relying only on human labels.

Core Features & Use Cases

Self-Critique and Revision: Generate an initial answer, critique it against a constitution of principles, and revise it for better safety and quality.
RLAIF Training Workflow: Create AI preference pairs, train a reward model, and optimize responses with reinforcement learning from AI feedback.
Safety Alignment Scenarios: Useful for reducing toxic, evasive, or poorly justified outputs in chatbots, assistants, and other LLM-based products.

Quick Start

Use this skill to evaluate a response against a constitution, generate a critique, and rewrite it into a safer and more helpful answer.

Frequently Asked Questions about constitutional-ai

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is constitutional AI and how does it improve LLM safety alignment?▼

Constitutional AI improves LLM safety alignment by using self-critique and revision against a rule-based constitution, generating AI preference feedback instead of relying solely on human labels to produce safer responses.

How do I train a reward model using RLAIF and AI-generated preference pairs?▼

To train a reward model with RLAIF, generate AI preference pairs by critiquing initial responses against constitutional principles, then use these comparisons to train the reward model for PPO-style optimization.

Can I use self-critique and revision for supervised fine-tuning without human labels?▼

Yes, self-critique and revision support supervised fine-tuning by generating an initial answer, critiquing it against a constitution of principles, and rewriting it into a safer and more helpful response without human labels.

Does PPO-style optimization work with AI-generated preference feedback for safety alignment?▼

PPO-style optimization works with AI-generated preference feedback by applying reinforcement learning from AI feedback to optimize model responses based on a reward model trained through constitutional principles.

When should I use RLAIF instead of human-labeled reinforcement learning for safety alignment?▼

Use RLAIF instead of human-labeled reinforcement learning when reducing toxic, evasive, or poorly justified outputs in chatbots and assistants requires scalable AI-generated preference feedback over costly human labels.

What are the limitations of using self-critique for reducing toxic outputs in LLMs?▼

Self-critique for reducing toxic outputs requires a well-defined rule-based constitution and depends on AI-generated preference comparisons, meaning the quality of safety alignment is bounded by the constitutional principles provided.