How to Make Enterprise RAG Systems More Reliable

Retrieval-augmented generation rests on a simple, sound idea: instead of asking a language model to answer from memory, let it look things up first. Fetch the relevant documents, put the evidence in context, and generate with the receipts on the table. The pattern, formalized by Lewis and colleagues in 2020, moved from research architecture to default enterprise design faster than almost any technique I can name.

So why do so many enterprise RAG systems disappoint the people who run them?

I recently surveyed the research literature on RAG architecture as it is actually deployed inside companies—retrieval infrastructure, graph-based variants, evaluation, and production operations. My conclusion was not the fashionable one. The model is rarely the bottleneck. The library is. Enterprise RAG does not need a bigger brain; it needs a safer, fresher, better-watched collection of documents, and an honest account of what each answer costs. Four gaps matter most,...

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