Why PDFs break LLMs — and what clean Markdown fixes
A PDF that looks perfect to a human often falls apart the moment a model reads it. Here's what goes wrong, and why converting to clean Markdown first makes downstream AI far more reliable.
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Le blog de markitdown.ai rassemble des notes pratiques sur la conversion de PDF, de fichiers Office, de numérisations et de pages web en Markdown pour les modèles, les pipelines RAG et les agents : les défaillances que nous observons et la façon dont la conversion les corrige.
A PDF that looks perfect to a human often falls apart the moment a model reads it. Here's what goes wrong, and why converting to clean Markdown first makes downstream AI far more reliable.
Retrieval quality is decided long before the vector database. This is a pre-processing checklist for turning messy source documents into clean, chunk-ready text.
Plain text throws away the structure a model could have used. Markdown keeps it, at almost no token cost — and that changes how reliably an LLM can reason over your content.
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