Convert any file to LLM-optimized markdown.

Building RAG pipelines

Convert PDFs, DOCX, and spreadsheets into clean, chunked markdown ready to embed into Pinecone, Weaviate, or any vector database. The token-optimized output reduces embedding costs by up to 60%.

Feeding context to Claude or ChatGPT

Instead of uploading raw files and hoping the model extracts what it needs, paste clean markdown directly. Less noise means the model focuses on your actual content, not structural artefacts.

Processing research papers and reports

Academic PDFs repeat headers, footers, and citations across every page. markitdown strips the repetition and gives you a clean linear text — ready to summarise, analyse, or query.

Preparing data for fine-tuning

Training datasets need clean, consistent text. Convert a folder of DOCX or PDF documents to uniform markdown, then feed them into your fine-tuning pipeline without manual cleanup.

XML tags like <document> and <section> match how Claude was trained to parse long-context documents. Anthropic recommends this structure in their own prompt engineering guide.

Best for: Long documents, RAG, structured analysis

YAML frontmatter and standard ATX headings (## H2) match GPT-4o's markdown training. XML tags add noise. Aggressive empty-line stripping saves tokens without losing structure.

Best for: Chat completion, code tasks, summarisation

With a 1M token context window, chunking is rarely needed. Clean prose with consistent heading hierarchy is sufficient — Gemini handles long continuous documents better than most models.

Best for: Very long documents, whole-codebase analysis