LLM-ready output
PDF to Markdown for ChatGPT & LLMs
Convert PDFs into compact, structured Markdown that ChatGPT, Claude and RAG pipelines can parse reliably — all processed locally in your browser.
Drop a PDF for LLM-ready Markdown, or
click to browseSee it in action
- 01
Upload a report or paper PDF, or click "Try sample PDF" to try our demo article.
- 02
Copy the Markdown output — it includes YAML metadata and chunk markers for RAG pipelines.
- 03
Paste into ChatGPT or Claude for Q&A, or split on chunk markers and embed into your vector database.
title: Getting Started with RAG pages: 1 format: markdown usage: llm-rag
Getting Started with RAG
Retrieval augmented generation combines a language model with an external knowledge base.
<!-- chunk -->Why Markdown
Markdown is compact and structured, which makes it the preferred input format for large language models.
Token-efficient
Markdown is far more compact than raw PDF text dumps, so you fit more context into the model’s window.
Structure the model understands
Clean headings and lists give the model document hierarchy, which improves grounding and reduces hallucination.
Ready for RAG
Predictable Markdown is easy to chunk and embed into a vector database for retrieval augmented generation.
Who uses it
Prompt engineers
Paste compact Markdown into ChatGPT or Claude to fit more context per prompt.
RAG developers
Chunk and embed clean Markdown for reliable retrieval augmented generation.
Data teams
Standardize messy PDFs into structured text for downstream AI pipelines.
Frequently asked questions
Are my files uploaded to a server?
No. Conversion runs entirely in your browser using WebAssembly. Your PDF never leaves your device, which makes it safe for contracts, research and other private documents.
Why is Markdown better than raw PDF text for LLMs?
Markdown encodes structure (headings, lists, tables) with very few extra tokens, which helps the model interpret the document and keeps prompts small.
Can I use the output in a RAG pipeline?
Yes. The clean Markdown is straightforward to split into chunks and embed for retrieval, with headings acting as natural chunk boundaries.
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