Retrieval-augmented generation is table stakes, not a finish line. Here's what it actually takes to ship enterprise copilots that stay grounded: retrieval quality, evals, guardrails and feedback loops.
The RAG honeymoon ends fast
A vector database and a prompt will get you a convincing demo in an afternoon. Production is where it gets hard: retrieval returns near-misses, the model fills the gaps with confident guesses, and a single hallucinated policy number erodes trust faster than ten good answers build it.
Grounding is a system, not a feature
Reliable copilots layer several defenses: high-recall retrieval with reranking, citations the user can verify, answerability checks that say 'I don't know' when context is thin, and structured extraction for the facts that must be exact. No single technique is enough on its own.
Measure what users actually feel
We score retrieval hit-rate, faithfulness and citation accuracy on a curated eval set before anything ships — then watch the same metrics live. Offline evals catch regressions; production telemetry catches the long tail that only real users find.
Close the loop
Every thumbs-down, every escalation to a human and every corrected answer becomes signal for the next iteration. The copilots that compound in value are the ones wired to learn from their own mistakes.