Reviews and benchmarks of content-moderation and safety tooling for LLM applications. Llama Guard, NeMo Guardrails, OpenAI Moderation, Perspective API, custom classifier patterns — what works, what regresses, what costs more than it saves.
Jigsaw's Perspective API has 8+ years of production data on toxicity detection. For community content moderation it remains strong. For LLM application safety it was never designed for this use case and it shows.
RAG pipelines have a moderation problem at the retrieval layer that input/output classifiers don't address. Injected content in retrieved documents can override model behavior. Here's the architecture that covers it.
Single classifiers have characteristic failure modes. Ensembles that combine models with different architectures and training distributions reduce coverage gaps. How to build and operate them.
False positive rates in content moderation are usually discussed as a technical metric. The business costs — user abandonment, manual review queues, appeal escalations — are rarely quantified. Here's how to measure and manage them.
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