This paper details "Constitutional AI," a novel method for training AI assistants to be harmless without extensive human-labeled data for harmful outputs. This approach involves a supervised learning phase, where an AI critiques and revises its own responses based on a set of pre-defined principles or a "constitution." Following this, a reinforcement learning (RL) phase uses AI-generated feedback to train a preference model, which then guides the AI to produce more desirable outputs, a process referred to as "RL from AI Feedback" (RLAIF). The key motivations behind this method include scaling AI supervision, creating a non-evasive yet harmless AI, and improving transparency in AI decision-making by leveraging chain-of-thought reasoning. Ultimately, Constitutional AI aims to achieve precise control over AI behavior with significantly less direct human oversight.

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