constitutional-ai

Constitutional AI trains models to be harmless using a constitution-driven framework and AI-generated feedback. It applies self-critique and revision phases, enabling safety alignment without requiring human labeling for every scenario.

Reinforcement learning from AI feedback (RLAIF) follows a supervised phase of self-critique and revision. Models evaluate and adjust outputs against explicit constitutional principles, then optimize behavior through AI-generated feedback using TRL tooling.

Yes, you can achieve scalable safety for chatbots and assistants without heavy human annotation. By defining explicit constitutional principles, the model self-critiques and revises outputs, reducing reliance on manual labeling across scenarios.

Implement RLAIF with TRL by configuring a model to follow a defined constitution. Begin with supervised self-critique and revision, then transition to reinforcement learning from AI feedback to steer behavior and explain objections.

A constitution-driven framework is needed for safety-critical AI deployments in content moderation and assistants. It provides explicit principles to steer behavior and explain objections, ensuring scalable safety without human labeling.