Why raw PDFs waste your context window
Paste a ten-page PDF into ChatGPT and half of your tokens go to page headers, citation footers, and mangled table fragments — context the model can't use. The AI-Ready Markdown card preprocesses documents the way RAG pipelines want: it strips running headers and footers, keeps every table intact instead of splitting it across chunks, and emits clean hierarchical Markdown with headings, lists, and fenced code blocks.
Built for vector pipelines too
Beyond chat prompts, the card exports a structured JSON AST for embedding and retrieval systems, so your vector store indexes semantics rather than layout noise. It pairs naturally with the universal OCR card when you need other formats, and with Messy PDF to Clean Word when a human editor joins the loop.
Better input, better answers — document quality is still the highest-leverage RAG optimization.
A quick checklist before you upload
One minute of prep multiplies the payoff. If the source is a scan, run it through the universal OCR suite first so there's a real text layer to parse. Keep multi-column papers whole — the layout model reorders them, so cropping rarely helps. Decide your chunking strategy up front: Markdown headings make natural chunk boundaries, while the JSON AST suits token-aware splitters and embedding pipelines. And when a human editor will touch the output, generate a Messy PDF to Clean Word copy alongside it — same source file, editor-friendly result, zero extra uploads.
Finally, remember that retrieval quality is judged by what the model sees at answer time: a clean hierarchy means the retriever surfaces the right section, while mangled headers mean it surfaces noise. Test with three or four real questions from your users after the first conversion — if the answers cite the right sections, the pipeline is ready; if not, the fix is usually as simple as re-running the source through a cleaner card.