AWS publishes prompting guide for Amazon Nova 2 Lite content moderation using MLCommons taxonomy

AWS published a technical guide for prompting Amazon Nova 2 Lite for content moderation tasks without requiring training data or model fine-tuning. The approach allows organizations to update moderation policies by editing prompts rather than retraining models.

Prompting approach and taxonomy

The guide uses the MLCommons AILuminate Assessment Standard v1.1, which provides a 12-category hazard taxonomy organized into three groups: Physical hazards (Violent Crimes, Suicide and Self-Harm), Non-Physical hazards (Non-Violent Crimes, Hate, Privacy), and Contextual hazards (Specialized Advice). AWS notes organizations can substitute their own custom moderation policies while maintaining the same prompt structure.

The content moderation pipeline operates in four stages: content ingestion, prompt assembly with system role and policy definitions, inference through Amazon Nova 2 Lite on Amazon Bedrock, and output processing that returns a violation flag, violated categories, and explanation.

Technical configuration

AWS recommends default inference settings of temperature 0.7 and top-p 0.9 for balancing output consistency with content diversity. For high-throughput pipelines, AWS suggests disabling reasoning mode to reduce latency and cost, though organizations should test both configurations for their specific use cases.

The guide provides two structured prompting formats: XML and JSON. The XML approach wraps policy definitions, content, and output fields in tagged sections with few-shot learning examples. Few-shot learning includes example input-output pairs in the prompt so the model learns expected response patterns.

Benchmark methodology

According to AWS, the guide includes benchmarks of Amazon Nova 2 Lite's content moderation performance against several foundation models across three public datasets, though specific benchmark results are not disclosed in the published excerpt. The company positions Amazon Nova 2 Lite as suitable for high-throughput moderation due to its low cost and fast inference capabilities.

What this means

This release demonstrates AWS's focus on prompt-based customization over fine-tuning for content moderation workloads. By building on the MLCommons standard, AWS provides a starting taxonomy that organizations can adapt without model retraining infrastructure. The structured prompting approach addresses a practical deployment challenge: content policies change frequently, and prompt editing offers faster iteration than model updates. The choice to recommend reasoning mode as optional for high-throughput scenarios reflects the latency-accuracy tradeoff that moderation systems face at scale. Organizations running moderation pipelines should test both reasoning-enabled and reasoning-disabled configurations against their specific content distribution and policy requirements before production deployment.