How to Make PDFs RAG-Ready
A practical guide to preparing PDFs for retrieval-augmented generation with layout-aware JSON, Markdown, headings, tables, reading order, and citations.
Start with structure, not plain text
Plain text extraction often loses headings, columns, table relationships, captions, and page context. RAG ingestion should preserve document structure so chunks align with the way people understand the source.
- ✓Heading hierarchy
- ✓Page numbers
- ✓Reading order
- ✓Tables as rows and cells
- ✓Figure and caption context
Chunk by document semantics
Use sections, headings, list boundaries, and table boundaries as chunking signals. Avoid splitting in the middle of a table or merging footer text into every chunk. Keep source metadata attached to each chunk.
Keep citations connected to pages
Every answer should be traceable back to source pages. Bounding boxes and page references let applications highlight evidence, support human review, and reduce trust gaps in generated answers.
Store both Markdown and JSON
Markdown is convenient for LLM ingestion. JSON is better for systems that need typed elements, coordinates, tables, page references, and review interfaces. A strong pipeline keeps both.
What to verify before publishing
- 1Parse headings and reading order
- 2Extract tables as structured rows
- 3Keep page and bbox metadata
- 4Generate Markdown for ingestion
- 5Store JSON elements for citations and review
- 6Index with document_id and task_id provenance
Questions about this guide
Why is plain PDF text not enough for RAG?+
Plain text loses layout, table structure, page context, and sometimes the intended sequence. That makes chunks harder to trust and cite.
Do I need bounding boxes for every RAG app?+
Not always, but bboxes are valuable when users need visual citations, source highlighting, or review workflows.
Should I index Markdown or JSON?+
Often both: Markdown for LLM-friendly text and JSON for metadata, typed elements, tables, and coordinates.