Five Pipeline Flaws That Make RAG Systems Return Wrong Answers

Retrieval-Augmented Generation (RAG) systems often produce incorrect outputs not because of the language model itself, but due to errors in the data pipeline that feeds it. Poor chunking strategies — such as splitting documents by fixed character counts rather than logical structure — can break meaning across fragments, making relevant content unretrieval. Weak embedding models may fail to match semantically equivalent but differently worded queries to their correct document passages. Vector search alone struggles with exact-string lookups like error codes or product IDs, a gap that hybrid keyword-plus-vector search can address. Finally, skipping a re-ranking step risks discarding the most relevant chunks before the model ever sees them, and even the order in which chunks are presented in the prompt can affect response quality.