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From PDFs to Answers: Docling + RAG in Practice

If your organization runs on PDFs, slides, and spreadsheets, a great Document AI pipeline starts with reliable parsing. In this post, I show how I pair Docling for robust structure extraction with a retrieval‑augmented generation (RAG) stack to answer domain questions with citations.

Why Docling + RAG

  • Docling normalizes messy documents into structured text, headings, tables, and figure captions.
  • RAG enables grounded answers, linking back to the original source for trust.

Architecture at a Glance

  • Ingest: Watch folder → parse with Docling → normalized schema
  • Chunk: Section‑aware windows with metadata (page, heading, table ids)
  • Index: Vector DB + BM25 for hybrid retrieval
  • Generate: Compose answers with citations and refusal patterns

Tips That Matter

  • Prioritize section‑aware chunking; avoid splitting tables mid‑row
  • Store source anchors early; don’t try to “reconstruct” later
  • Use hybrid retrieval for both recall and precision

I’ve packaged this thinking into my document intelligence case study, which I adapt per client corpus and compliance needs.