The demo always works. Someone wires a vector index to a foundation model in a notebook, asks it three questions about the employee handbook, gets three crisp answers, and the room nods. Then the request becomes "ship it to 4,000 employees," and the notebook quietly dies. There's no endpoint, no auth, no version history, no way to see why a particular answer was wrong, and no story for when legal asks how you'll roll back the prompt that started citing the 2019 PTO policy.I've watched several teams hit this wall. The RAG part — chunk, embed, retrieve, stuff the context, generate — they understand cold. What they're missing is the boring half: how do you turn a chain that runs in a notebook cell into a governed, versioned, monitored REST endpoint that a chat UI can call, that survives a 3 a.m. page, and that you can A/B test next month without redeploying the universe? This article is about that boring half. We'll assume you know RAG conceptually and walk the full Databricks path: author the chain, log it to MLflow, register it to Unity Catalog, deploy it to a Model Serving endpoint, trace every retrieval and generation, and operate the thing once it's live.