An enterprise playbook for RAG that actually retrieves the truth
A field-tested architecture for grounding LLMs in your own documents, contracts and tickets, with evaluation patterns you can run in production.
On this page
Start with the documents people already trust.
High-performing RAG systems begin with source ownership. Contracts, SOPs, support tickets, and policy documents need clear freshness rules and review workflows before they enter a vector index.
Build the eval harness before the executive demo.
A useful RAG system needs repeatable tests for relevance, citation accuracy, refusal quality, and latency. Without this harness, teams cannot tell whether a model change improved retrieval or only sounded better.
- Create golden questions from real user journeys.
- Score citations separately from generated prose.
- Track retrieval misses as product backlog, not model failure.
Make corrections flow back into the corpus.
Reviewer surfaces should capture wrong answers, missing sources, and stale documents. The system compounds when operations teams can repair knowledge without waiting for a full engineering cycle.
Download the full whitepapers package.
Includes the working model, executive summary, and the architecture references referenced in this piece.
