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Document parsing and ingestion for RAG: PDFs, tables, OCR

  • RAG
  • Specialist topic or differentiator
  • About 75 min to learn
  • 3 vetted resources
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What this topic covers

Many RAG failures that look like retrieval problems start at ingestion. Pages get flattened into one stream with the wrong reading order, tables lose their structure, or scanned pages come out empty. Interviewers ask this to see whether you debug from the source upwards. A strong answer compares plain text extraction, layout-aware parsing and OCR, and explains when a vision-language model, or skipping text extraction and embedding page images, is worth the cost. It also covers keeping metadata and access-control labels attached to every chunk, and re-indexing only what changed when documents are updated.

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You’re ready when you can

What interviewers check, from the resources below.

  • Point to a reading-order or table error in plain text extraction that the layout-aware output fixes
  • Explain when OCR is needed (scanned pages) and what it costs in time
  • Describe how you would keep page number, section heading and access labels on each chunk
  • Explain how you would re-index only documents whose content hash changed
  • Contrast fast text extraction, layout-model (hi_res) parsing and OCR-only parsing
  • Explain why table extraction needs the slower layout-aware strategy
  • Say how you would route documents to a strategy automatically
  • Explain how late interaction over image patch embeddings works at a high level
  • Compare indexing cost and query latency against an OCR-plus-text-embedding pipeline
  • Say when you would still parse to text (exact quotes, citations, keyword search)

What to study, in order

Each link opened and checked by hand; the date says when.

  1. Start here

    Docling (opens in a new tab)

    GitHub (docling-project) · 60 min · Free · checked 21 Oct 2026

    Why
    Running a layout-aware parser on a messy PDF is the fastest way to see why naive extraction breaks retrieval. It gives you a concrete story for the interview.
    What to read
    Read “What is Docling ?” and “Features”, then do the “Quickstart” (install, convert via the CLI, then the Python usage). Convert a PDF with a table and two columns, export it to Markdown, and compare the result with plain pypdf text from the same file.
    What to skip
    Skip “Integrations”, “Contributing” and the LF AI & Data section. You don’t need the VLM pipeline unless you have a GPU to hand.

  2. Free alternative

    Partitioning (opens in a new tab)

    Unstructured · 25 min · Free · checked 21 Oct 2026

    Why
    Its parsing strategies line up neatly with the speed-versus-fidelity trade-off you’ll be asked to explain.
    What to read
    Read the general “partition” section and the document-type table showing which formats support tables, then read “partition_pdf” and “partition_image”. Note what the auto, fast, hi_res and ocr_only strategies each do, and that tables come out as HTML.
    What to skip
    Skip the partition functions for formats you won’t ingest (epub, org, rst, rtf, msg and similar).

  3. Go deeper

    ColPali: Efficient Document Retrieval with Vision Language Models (opens in a new tab)

    arXiv · Manuel Faysse et al. · 45 min · Free · checked 21 Oct 2026

    Why
    It is the strongest case for skipping text extraction altogether and embedding page images. Interviewers increasingly raise this as an alternative to parsing pipelines.
    What to read
    Read “1 Introduction”, “2 Problem Formulation & Related Work”, “3 The ViDoRe Benchmark”, “4.1 Architecture” and “5 Results” (especially 5.2, latencies and memory footprint).
    What to skip
    Skip “4.2 Model training”, the ablation study and appendices B–F.

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