Retrieval-Augmented Generation looks simple in a demo: embed your documents, retrieve the top-k matches, stuff them in a prompt. Production RAG systems that hold up under real enterprise query patterns need a lot more nuance in the retrieval layer.
Why naive top-k retrieval breaks down
Pure vector similarity search retrieves semantically similar chunks, but similarity isn’t the same as relevance — a chunk can be topically similar to a query while containing none of the specific facts needed to answer it.
Enterprise document sets also have structure (headings, tables, cross-references) that flat chunking destroys, which is why so many RAG pilots perform well on FAQ-style questions and poorly on anything requiring multi-hop reasoning across documents.
Patterns that hold up in production
Hybrid retrieval — combining vector similarity with keyword/BM25 search and re-ranking the merged results — consistently outperforms pure vector search on enterprise document sets with lots of domain-specific terminology and acronyms.
Hierarchical chunking that preserves document structure (and retrieves parent context alongside a matched child chunk) meaningfully improves answer quality on documents like policy manuals, contracts, and technical specifications.
Governance can’t be an afterthought
Retrieval needs to respect the same access controls as the source documents — a RAG system that can surface content a user wouldn’t otherwise be authorized to see is a data governance incident waiting to happen, not a feature.
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