Convert Documentation to Markdown for LLM Context Windows
Large language models like ChatGPT, Claude, and Gemini work best when you feed them clean, structured text. Product documentation on the web is HTML wrapped in navigation bars, sidebars, footers, cookie banners, and JavaScript that has nothing to do with the content.
Pasting raw HTML into a prompt spends context-window tokens on that boilerplate. Converting to Markdown first removes the noise and leaves the model just the content.
Why Markdown for LLMs
- Token efficiency: Markdown uses about 60% fewer tokens than the equivalent HTML for the same content
- Structure preserved: headings, lists, tables, and code blocks map straight to Markdown syntax
- No distractions: the density algorithm removes navigation, ads, and layout markup
- RAG-ready: clean Markdown chunks well for embedding and retrieval-augmented generation
How it works
- Mirror the docs: use
wget -r -np -kto download a documentation section as HTML files - ZIP and upload: bundle the HTML files into a ZIP archive and drop it on the converter
- Download Markdown: get a ZIP of clean
.mdfiles ready to paste into your LLM workflow or index in a vector database
Typical uses
- Loading API reference docs into Claude Projects or ChatGPT custom GPTs
- Building a RAG knowledge base from product manuals
- Creating a searchable Markdown archive of framework documentation for offline AI coding assistants
- Preparing training data from technical wikis
Privacy first
Your documentation never leaves your browser. The conversion runs locally through WebAssembly, so nothing is uploaded or stored on a server. That makes it a fit for proprietary or internal documentation.