Your RAG Pipeline Is Only as Good as Its Ingestion Every team building retrieval-augmented generation hits the same bottleneck, and it is not the vector database, the embedding model, or the retrieval algorithm. It is the step before all of those: getting clean text out of the source documents. You have a pile of PDFs, Word documents, scanned contracts, and spreadsheets. Your RAG pipeline needs them as text. What sits between the file and the embedding model is always messier than anyone budgets for. The naive approach — run an OCR library, strip the markup, split on newlines — produces output that looks plausible until you inspect it. Tables collapse into jumbled strings. Multi-column layouts get interleaved. Headers from page footers land in the middle of paragraphs. Scanned pages return empty strings with no error. The result is bad chunks, bad embeddings, and bad retrieval. The LLM confidently answers questions using garbage context, and nobody notices until a customer complains.…