Building Production-Ready AI Vector Search in Databricks: Chunking, Embeddings, ML Pipelines, and RAG

The demo always works. You paste a PDF into a notebook, split it into chunks, embed them with a hosted model, push the vectors into an index, and ask a question. The answer comes back, everyone nods, and the prototype gets a thumbs-up in the team channel. Then someone asks the question that ends most RAG projects: what happens when the document changes?

I've watched teams ship a vector search prototype in an afternoon and then spend three months turning it into something they could actually run. The hard part was never the similarity math. It was the boring operational reality underneath it: documents that get edited nightly, an index that quietly goes stale, an embedding model that gets a new version, and a governance team that wants to know exactly which rows fed which answer.