If you moderate user-generated content at scale, you need a system that catches policy violations accurately without over-flagging legitimate posts. A moderation system that misses harmful content puts you at risk, while one that flags too aggressively frustrates your audience. Every organization defines its own policies, so a single classifier rarely works for every use case. In a previous post, we showed how to fine-tune Amazon Nova for content moderation tasks using Amazon SageMaker AI. Prompting requires no training data or model customization, so you can update your moderation policies by editing the prompt rather than retraining a model.

In this post, you learn how to prompt Amazon Nova 2 Lite for content moderation using structured and free-form approaches, grounded in the MLCommons AILuminate Assessment Standard. The prompting techniques use the AILuminate taxonomy as an example, but they work equally well with your own custom moderation policy. You can swap in your own category definitions and the prompt structure stays the same. We also benchmark the content moderation capabilities of Amazon Nova 2 Lite against several foundation models (FMs) on three public datasets.

The MLCommons AILuminate assessment standard