RAG at Enterprise Scale
Evaluate chunking, retrieval, citations and operating tradeoffs on your own workload
By
Tenten AI Research
AI Infrastructure
Published
Updated
Read time
7 min
Abstract
An enterprise retrieval-augmented generation (RAG) system needs evidence that its answers use the right documents, respect access rules and meet the task's operating limits. A configuration that works for one corpus may fail on another.
This guide proposes six areas to evaluate and a compact release record. The examples and checks are editorial suggestions, not client results or universal performance targets. Choose acceptance conditions with the system owner, then compare candidates on the same authorized workload.
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