Deploying an Enterprise LLM Chatbot on Databricks With RAG, MLflow, Vector Search, and Model Serving

The useful part

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.

How it works

• 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.
• 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...
• The RAG part — chunk, embed, retrieve, stuff the context, generate — they understand cold.
• page, and that you can A/B test next month without redeploying the universe?