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RAG 8 min read

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.

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