If you’ve built a RAG (Retrieval Augmented Generation) system, you’ve probably hit this wall: your vector search returns 20 documents that are semantically similar to the query, but half of them don’t actually answer it. A user asks “how do I handle authentication errors?” and gets back documentation about authentication, errors, and error handling in embedding space, but only one or two are actually useful. This is the gap between demo and production. Most tutorials stop at vector search. This reference architecture shows what comes next. This AI Search reference app shows you how to build a production grade enterprise AI search using Heroku Managed Inference and Agents . Why two-stage retrieval Vector embeddings are coordinates in high dimensional space. Documents close together share semantic meaning. Semantic proximity is a false proxy for accuracy; a document can be ‘close’ in vector space but fail to provide a factual answer.…