pdf-ingestion

Documents

Get a PDF into the model without blowing the context window or losing structure. Native PDF beats OCR-then-text for most cases; extract-then-summarize beats native for very long docs.

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PDF Ingestion

Three ways to feed a PDF to the model, in increasing order of preprocessing:

Native PDF input — pass the file directly. Model sees pages as images + extracted text. Best for docs under ~100 pages with meaningful layout (tables, figures, forms). Preserves structure.
Text extraction then send — pdftotext / pypdf / equivalent, then send the text. Loses layout but cheap. Fine for prose-heavy docs where tables don't matter.
Extract → chunk → summarize → send — for docs >100 pages or when you'll query the same doc many times. Preprocess once, cache the summary.

Deciding which path

Doc shape	Path
<20 pages, layout matters (report, form, invoice)	Native
<20 pages, pure prose (article, memo)	Text extraction
20-100 pages, mixed	Native, but chunk if context tight
>100 pages	Extract → chunk → summarize
Scanned PDF (no text layer)	OCR first (Tesseract or vision model), then treat as extracted text
Tables are the point	Native — text extractors mangle tables
Figures/diagrams are the point	Native + explicit "describe the figure on page N" prompt

Native PDF — the good defaults

Cache the PDF at a prompt-caching breakpoint (see prompt-caching). Native PDFs are large — every uncached turn costs full input price on the whole doc.
Ask about specific pages ("summarize section 3.2 on page 14") rather than the whole doc. The model handles targeted queries better than "summarize this 80-page report".
Follow up with page-cited claims — "on which page does the doc say X?" — as a sanity check the model isn't hallucinating.

Extract-then-send — the traps

pdftotext reading order. Multi-column PDFs come out as interleaved lines. Use pdftotext -layout for column preservation, or pdftotext -raw for straight reading order — pick per doc, don't guess.
Tables become word soup. If tables are load-bearing, native or per-table image extraction. Not text.
Headers/footers repeat on every page. Strip them before sending, or the model will treat them as content.
Footnotes drift to random positions in the extracted stream. Filter or accept the noise.

Extract → chunk → summarize (long docs)

Chunk by section, not by token count. A section-aware split respects the doc's logic; a naive 4K-token split cuts sentences and tables.
Summarize per section into a "map" — 1-2 paragraphs each. Keep the map short enough to fit in context whole (~2-4K tokens for a 200-page doc).
Store the full section text alongside the map (paths in a manifest). Fetch on demand when a question needs detail beyond the summary.
Cache the map at a prompt-caching breakpoint so multi-turn Q&A over the doc doesn't reprocess.

Red flags

Sending a 200-page PDF native to answer one question. Extract the relevant page range first.
Trusting the text extractor on a form or invoice. Layout carries meaning. Use native.
OCR'ing a PDF that already has a text layer. Check pdftotext -q file.pdf - first — if text comes out, no OCR needed.
No page citations in output. Model can hallucinate confidently across long PDFs. Force page numbers into the response format.
Re-uploading the same PDF every turn without caching. Cost climbs linearly; a 5-minute cache fixes it.

Loopkit-adjacent

If the PDF is a spec, extract it into PROMPT.md via spec-first — the agent should re-read prose, not re-scan the PDF, on every turn.