Most RAG Systems Don’t Fail Because of the LLM
Production RAG breaks at retrieval, ranking, freshness, and evaluation — not just prompting.
Most RAG systems do not fail because the model is weak.
They fail because the retrieval layer is under-designed.
A demo RAG pipeline can work with a simple flow:
Upload documents
Split them into chunks
Generate embeddings
Store vectors
Send retrieved text to the LLM
That is enough for a prototype.
But once real users start asking messy, ambiguous, domain-specific questions, the system starts breaking in different places: retrieval quality, ranking, stale embeddings, missing metadata, weak citations, and poor evaluation.
That is where RAG becomes an engineering problem, not a prompting problem.
Full deep dive
This is a short field note.
I wrote the full breakdown with chunking strategy, embedding pipeline design, hybrid retrieval, re-ranking, Azure architecture, evaluation, and production mistakes here: