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What are the challenges in ingesting and parsing documents for RAG?
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Common problems and fixes
| Problem | Effect | Fix |
|---|---|---|
| Multi-column PDFs | Text from two columns interleaved | Layout-aware parsing (e.g. Unstructured, Docling, LlamaParse, Azure Document Intelligence, AWS Textract) |
| Tables | Flattened into a meaningless number soup | Extract as Markdown/HTML; keep header with rows; or summarise each table (Q23) |
| Scanned documents | No text at all | OCR or vision-language models |
| Charts and diagrams | Information lost | Caption with a vision model; multimodal RAG (Q34) |
| Headers, footers, page numbers | Noise in every chunk | Detect and strip repeating elements |
| Slides | Fragmented bullets, little context | Combine title + bullets + speaker notes per slide |
| Spreadsheets | Not prose | Route to structured query (text-to-SQL / pandas) |
| Duplicate documents | Duplicate results, wasted context | Hash-based and near-duplicate detection |
Process advice
- Look at the parsed output for a sample of each document type before building anything else. Many teams skip this and debug retrieval for weeks.
- Keep page numbers and section paths as metadata for citations.
- Build parsing as a pipeline per document type, with quality checks (e.g. a ratio of non-alphabetic characters to flag garbage).
Interview line. "Garbage in, garbage retrieved. I'd spend the first days on parsing quality, because it caps everything downstream."
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