Reliable RAG starts with clean ingestion
Most retrieval failures begin when documents are turned into messy text. LLMtoMD gives your pipeline structured Markdown and ready-to-index chunks instead.
Why RAG pipelines underperform
A RAG system is only as good as the text it retrieves. When PDFs and Office docs are flattened into structureless text, tables collapse, headings vanish, and reading order scrambles.
Your embedding model can't tell a heading from body text, and at answer time the LLM fills the gaps with confident guesses — the hallucinations you've been blaming on the model.
How LLMtoMD fixes it
Layout-aware Markdown
Tables stay aligned, headings survive, and reading order is preserved — so chunks carry real meaning.
RAG-ready export
Export any document as chunked JSONL with embeddings — drop straight into your vector DB, LangChain, or LlamaIndex.
Vision for diagrams
Charts and diagrams are described instead of dropped, so the densest parts of a page make it into the index.
Automated ingestion
Push files via the API or watch a storage prefix so new documents convert and index themselves.
Related reading: Why Your RAG Bot Hallucinates
Convert anything to AI-ready Markdown
PDFs, Office docs, images, audio, and whole websites — clean Markdown and RAG-ready exports for your LLM, in seconds.