RAG for enterprise knowledge search: what actually works
Vector databases are not magic. Real enterprise RAG needs hybrid retrieval, re-rankers, and a measurable eval suite. A field guide from real deployments.
By Saad Alam
RAG
RAG for enterprise knowledge search: what actually works
If your RAG system can't answer 'how good is retrieval today vs last week' with numbers, you don't have a system, you have a pipeline.
Hybrid search (BM25 + dense + re-rank) consistently outperforms pure vector search on enterprise content. The bias toward exact-token matching matters in legal, finance and engineering docs.
Chunking is underrated. Naïve fixed-window chunking destroys semantic units. Use semantic chunking, retain heading context, and store paragraph + heading metadata.
Build an eval set on day one. 100 question/answer/citation triples is enough to start. Every retrieval change ships with a regression report.
