Why most RAG systems fail in production (and how to avoid it)

Retrieval-Augmented Generation looks trivial in a notebook. You chunk some text, embed it, throw it in a vector database, and wire it up to an LLM. It works beautifully on the five examples you test it with. Then you put it in front of users, and it falls apart.

Production RAG is a search problem, not a generative AI problem. The failures almost always happen in the retrieval step. If you retrieve the wrong context, the LLM will hallucinate confidently. If you don’t handle metadata filtering, you’ll retrieve irrelevant chunks.

We build RAG systems by focusing relentlessly on the retrieval pipeline. We use hybrid search (keyword + semantic), rigorous chunking strategies tailored to the specific document structure, and programmatic evaluations to measure retrieval accuracy before we even touch the generation step.