AI BriefingPDFs That Change Content Depending on How You Read Them
Key point
This introduces a technique that uses the PDF's 'marked-content' property to simultaneously provide visual information for humans and markdown structure for LLMs.
Details
Existing PDFs use a visual coordinate-based format, which makes it difficult for LLMs to grasp structure (headers, lists, tables, etc.) since the structure must be reconstructed during text extraction.
This approach leverages the replacement text property from the PDF 1.4 spec. Renderers draw the visual elements as-is, while text extraction tools like PyMuPDF or Poppler are designed to return the specified Markdown structure instead.
Key features and advantages:
- Same file, dual output: Humans see a normal PDF, while LLMs read structured Markdown
- Improved information density: Token counts stay similar or increase slightly, but the clearer structural information improves LLM reasoning accuracy
- Low overhead: File size increase is very minimal (mostly within single-digit percentages)
- Compatibility: Major open-source PDF extraction libraries support this property
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