Production RAG: Why Pure-Semantic Retrieval Fails Enterprise Content
Why single-vector RAG underperforms on regulatory, legal, and technical content, and what hybrid retrieval, reranking, and evaluation actually deliver in production.
Pure-semantic RAG (a single embedding model, single vector store, top-k retrieval, stuff into prompt) fails on enterprise content because regulatory, legal, and technical documents contain exact terms, formula references, and specific clauses that semantic similarity alone misses.
The production pattern that works in enterprise environments is hybrid retrieval plus reranking. Hybrid means combining dense (semantic) retrieval with sparse (BM25 or similar lexical) retrieval, then merging the results. This catches both conceptually similar passages and exact-term matches. Reranking applies a cross-encoder to the top 50 results from hybrid retrieval and produces a final top 5 that is dramatically more relevant than either retrieval method alone.
Add a few more layers for production: query rewriting (use a small LLM to reformulate user queries into retrieval-optimised forms), chunk-level metadata filtering (use document type, recency, and access permissions), and citation enforcement (require the LLM to cite a chunk for every factual claim). With these layers, enterprise RAG hits the 90 percent+ accuracy threshold needed to clear internal risk review. Without them, you ship a demo, not a production system.