"Most Retrieval-Augmented Generation (RAG) projects don't fail because of the model; they fail because of the documents. Inside every organization sit thousands of PDFs, slide decks, and Word files designed for human eyes, not machines. Reading order gets scrambled, tables collapse into mush, and figure captions drift away from the paragraphs that reference them. Feed low-quality data into an embedding model and you'll get low-quality retrieval, which means answers you can't trust."
"The fix can be implemented at the point of ingestion, by treating documents as structured data. When you preserve hierarchy, layout, tables, lists, and captions, you give your downstream stack a faithful view of what the author wrote and how it was meant to be read. Doing this improves all the downstream applications that leverage your data. Docling is an open-source project built to help. It parses real-world documents - reports, contracts, decks, scans - into clean structures: headings, lists, tables, figures, captions, plus the order a human would read. It can also link text to visual elements (tables, figures, regions), which makes "show your work" UIs possible."
RAG failures often stem from poor-quality document ingestion rather than model shortcomings. Organizational documents are created for human reading, causing scrambled reading order, lost headings, flattened tables, and detached captions when naively extracted. These distortions produce low-quality embeddings and unreliable retrieval. A solution is to treat documents as structured data at ingestion by preserving hierarchy, layout, tables, lists, captions, and human reading order. Preserving these elements yields a faithful representation for downstream stacks and improves all applications that leverage the data. Docling is an open-source tool that parses real-world documents into clean structures and links text to visual elements to enable transparent UIs.
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