Use case

PDF to Markdown for Claude, ChatGPT & RAG pipelines

Feeding a chunk of raw PDF text (with its broken line-wraps, stray headers/footers, and mangled tables) into an LLM or a retrieval-augmented-generation index produces worse results than feeding it clean Markdown. Markdown preserves the structure a model actually needs — headings for context, tables as tables, lists as lists — without the noise.

Why Markdown specifically

Headings survive chunking. A RAG pipeline that splits documents into chunks needs section boundaries to stay meaningful — #/## headers give it that for free.
Tables stay tables. Raw PDF text extraction often turns a table into a wall of numbers with no column alignment. Markdown tables keep rows and columns machine-readable.
It's what every model was trained to read well. Markdown is the dominant format in code, documentation, and chat interfaces — Claude, GPT, and Gemini all handle it natively, without special-casing.
It's smaller. Stripped of PDF/DOCX formatting overhead, the same content is fewer tokens — see how much that actually saves.

Common setups this fits

Claude Projects / custom GPTs

Upload clean Markdown as reference material instead of raw PDFs — better retrieval, fewer tokens per query.

RAG / vector search

Chunk on Markdown headings instead of arbitrary character counts for cleaner, more relevant retrieval.

Note-taking (Obsidian, Notion)

Drop converted lecture slides, papers, or reports straight into a markdown-native vault.

Coding agents (Cursor, Claude Code)

Convert a spec or design doc PDF once, then let the agent reference the Markdown directly — or call SlimdownPDF's MCP server so the agent converts it itself.

Formats supported

PDF (scanned or digital), Word (.docx/.doc), Excel (.xlsx/.xls), PowerPoint (.pptx/.ppt), images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, YAML, ZIP archives, and YouTube video URLs — one converter, one output format.

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