constitutional-ai

Reinforcement learning from AI feedback (RLAIF) improves safety alignment by using a constitution to guide model self-critique and revision, enabling scalable preference learning for harmlessness without requiring human labels for harmful outputs.

To implement self-critique and revision, provide a prompt to generate an initial response, critique that response using a configurable constitution, and iteratively revise the output until safety alignment is achieved.

Yes, constitutional AI eliminates the need for manual labeling by teaching models to critique and revise their own outputs based on a constitution, using AI feedback for scalable safety alignment instead of human supervision.

Yes, you can use chain-of-thought prompts within the constitutional AI workflow to ensure transparent reasoning and auditability during the self-critique and revision process for model safety alignment.

The best way to achieve harmlessness without human supervision is using constitutional AI, which applies a constitution to generate self-critiques and revisions, supporting end-to-end workflows from supervised fine-tuning to reinforcement learning.

You should reconsider using reinforcement learning from AI feedback if your safety alignment task requires strict human-verified labels, as this approach relies entirely on AI self-critique and a configurable constitution to minimize human supervision.