Originally published on AI Tech Connect.
What you need to know Most retrieval-augmented generation systems that touch documents share the same opening move: run optical character recognition over the file, split the extracted text into chunks, embed each chunk into a single vector, and search those vectors at query time. It is a well-worn pipeline, and on clean, text-only documents it works fine. The trouble starts the moment the document is visually rich — a scanned claim form, a slide deck, a financial statement thick with tables, an engineering drawing with callouts. OCR flattens all of that into a linear stream of characters, and in doing so it loses the very structure that made the page meaningful. A number in a table means something because of the row and column it sits in; strip the layout and it becomes a floating digit.…
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