Large language models are text-first. Before a PDF can be summarized by ChatGPT, embedded into a vector database, or retrieved by a RAG pipeline, it has to become plain text - and how you do that conversion quietly decides how well everything downstream works.
Why Markdown beats raw text extraction
Naive text extraction gives you a wall of undifferentiated text. Markdown extraction preserves the document's structure:
- - **Headings become # markers** - so a chunking strategy can split on sections instead of cutting mid-paragraph
- **Lists stay lists** - so enumerated requirements or steps survive as coherent units
- **Paragraph boundaries are kept** - so embeddings represent complete thoughts
That structure is exactly what retrieval needs. When your chunker can split on headings, each chunk carries its own context ("## Termination Clauses" tells the model what the following text is about). When it can't, you get chunks that start mid-sentence, and retrieval quality drops in ways that are hard to debug later.
What typically breaks during PDF extraction
PDFs are a layout format, not a text format - the file stores positioned glyphs, not paragraphs. Common failure modes to check for after any conversion:
- - **Headers and footers** repeating on every page and polluting chunks
- **Multi-column layouts** interleaving text from both columns
- **Tables** collapsing into meaningless space-separated runs
- **Hyphenated line breaks** splitting words in the middle
- **Scanned pages** producing nothing at all (there is no text layer to extract - you need OCR first)
No converter handles every document perfectly. Spot-check the output on a few representative pages before you process a whole corpus.
A practical, private workflow
Our PDF to Markdown converter runs entirely in your browser. It detects headings from font sizes, preserves paragraph breaks, and can insert page-break markers so you can trace any chunk back to its source page.
- - Drop in your PDF
- Enable header detection and page breaks if you want them
- Download the .md file and feed it to your pipeline
Because processing is client-side, nothing is uploaded - which matters when the PDFs are contracts, medical records, internal strategy docs, or anything else you'd rather not send to a third-party converter just to prepare it for an LLM.
If you want structure you can program against instead - per-page text with positions and metadata - use PDF to JSON. For the simplest possible output, PDF to Text gives you a clean .txt file.
Markdown for note-taking apps
The same conversion works for Obsidian, Logseq, and other Markdown-based tools: convert a PDF once and its content becomes searchable, linkable notes. Heading detection means the resulting file gets a real outline in your editor's sidebar rather than one undifferentiated blob.
Bottom line
Extraction is the least glamorous step of a RAG pipeline and the one most likely to silently ruin it. Convert to Markdown rather than raw text, check the output on real documents, and keep confidential files local while you do it.