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A RAG system returns the wrong number for 'Q3 revenue in EMEA', even though that value sits in a table in an indexed PDF. You have a strong reranker and hybrid search. What is the most likely fix?

Fix ingestion so the table is extracted with its row and column structure intact, because retrieval cannot recover a fact the parser already flattened, Ingestion is upstream of every retrieval lever. If the parser flattened the table and broke the link between 88, EMEA, and Q3, then the fact was never indexed in a usable form. No reranker, larger retrieval set, or bigger embedding model can return a fact that extraction destroyed. The ceiling is the parser, so that is where the fix lives.

Why is flattening a table into a line of text so damaging for retrieval?

A table's meaning is two-dimensional: a cell only means something next to its row and column headers, and flattening destroys that link, so a value like 88 no longer connects to EMEA or Q3, The value in a table cell is defined by its position: its row header and its column header. Flatten the grid into one line and the numbers survive but the positions do not, so a query about Q3 revenue for a region retrieves a chunk where those facts no longer connect. Structure-preserving handling keeps each cell tied to its headers.

You have a mix of clean single-column memos, multi-column reports with tables, and scanned contracts. How should you handle extraction?

Climb the extraction ladder per document: plain text for the simple memos, layout-aware parsing for the multi-column reports and tables, and OCR or a vision model for the scans, Each rung of the ladder buys accuracy for cost and time. Plain text is fine for genuinely simple pages but loses columns and tables. Layout-aware parsing handles the reports. Scans have no embedded text, so they need OCR or a vision model. The goal is the lowest rung that keeps each document's facts intact, not the most expensive tool for everything.

What is the single validation step teams most often skip when building an ingestion pipeline, and why does it matter?

Reading the extracted text of your hardest documents by hand to confirm the facts survived, because ingestion failures are silent and throw no errors, Ingestion failures do not raise errors. A flattened table or a scrambled column runs green and only shows up as wrong answers later. Reading the extracted text of your worst documents, the dense tables and messy scans, is error analysis applied to ingestion. It is the cheapest way to catch a silently broken parser before it caps everything downstream.

Document Ingestion: The Layer That Quietly Caps Everything

Name: System Design Masterclass
Availability: InStock

in Retrieval Rag

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