Document parsing is the unglamorous step before RAG indexing: getting readable text out of real-world documents. PDFs especially are a nightmare — they store text as positioned glyphs, not logical paragraphs, and the reading order is frequently wrong. Scanned PDFs have no text at all and require OCR.
Popular tools: PyMuPDF and pdfplumber for basic PDF text extraction. Unstructured.io for mixed-format pipelines. Azure Document Intelligence or AWS Textract for complex layouts (tables, forms, multi-column). LlamaParse specifically for RAG-optimized extraction.
The rule of thumb: spend 20% of your RAG engineering time on parsing quality before spending anything on model tuning. Most RAG failures trace back to garbled extracted text, not retrieval or generation.
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