How to Build Reliable LLM-Based Content Moderation Using Embeddings and Reranking

A technical architecture for automated content moderation combines semantic search, reranking, and LLM classification to accurately label moderation reports against a defined topic taxonomy. The system retrieves relevant policy passages using embeddings, reranks them to surface the most pertinent evidence, and asks the LLM to classify only against those top passages rather than an entire policy handbook. Every output must conform to a strict JSON schema containing a known topic ID, a confidence score within an allowed range, and cited passage IDs, with invalid or ambiguous responses routed to an explicit unclassified state instead of a silent default. API errors such as HTTP 429 are treated as backpressure with bounded retries, while structurally invalid model responses get one repair attempt before being escalated for human review. The approach prioritizes structured output correctness and audit traceability over model novelty, ensuring reviewers can distinguish retrieval failures from classification failures.