constitutional-ai

Train AI safety through self-critique and AI feedback without human labels.

Updated Mar 16, 2026

Search Tags:#ai #safety #alignment #prompt-design #constitutional-ai #self-critique #rlaf

Ready to run

npx skills add https://github.com/arsity/scholar-tools --skill constitutional-ai-arsity

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: constitutional-ai
Source: https://github.com/arsity/scholar-tools/tree/main/vendor/ai-research-skills/07-safety-alignment/constitutional-ai
Command: npx skills add https://github.com/arsity/scholar-tools --skill constitutional-ai-arsity

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Trains AI to be harmless through a constitution-based framework of self-critique and AI feedback, eliminating the need for human-label data while maintaining safety.

Core Features & Use Cases

Formal principles (constitution) guiding model outputs toward helpfulness, honesty, and harmlessness.
Self-critique and revision loops to iteratively align responses with the constitution.
RL from AI Feedback (RLAIF) to scale safety improvements without human labels.
Optional chain-of-thought prompts for transparent safety reasoning.

Quick Start

Run the supervised learning workflow to generate initial responses, critique them with the constitution, and produce revised outputs for fine-tuning.

Frequently Asked Questions about constitutional-ai

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

FAQPage Schema

What is AI safety alignment via constitutional AI and self-critique?▼

AI safety alignment via constitutional AI trains models to be harmless through self-critique and AI feedback. It applies a constitution of formal principles guiding outputs toward helpfulness and honesty without requiring human labels.

How do I train AI to be harmless without human label data?▼

You train AI to be harmless without human labels by using Reinforcement Learning from AI Feedback (RLAIF). This workflow generates responses, critiques them against a constitution, and fine-tunes the model on revised outputs.

Can I use chain-of-thought reasoning prompts for transparent safety alignment?▼

Yes, you can use optional chain-of-thought reasoning prompts for transparent safety alignment. These prompts expose the model's safety reasoning process during the self-critique and revision loops.

Does reinforcement learning from AI feedback work for scaling safety without human labels?▼

Reinforcement learning from AI feedback scales safety improvements without human labels by using AI to evaluate and critique model outputs. It follows supervised learning with a constitution to iteratively reduce harmful outputs.

What are the limitations of using self-critique for prompt design and safety alignment?▼

The limitation of using self-critique for safety alignment is that the model's revision quality depends entirely on the defined constitution. If principles are incomplete, harmful outputs may not be fully corrected during the RLAIF phase.