Most developers building "chat with your data" apps hit the exact same wall. You chunk the text, embed it, dump it in a vector database, and the retrieval is still terrible. The model hallucinates or completely scrambles tables.
People think data ingestion is just text extraction. It isn't. In 2026, text extraction is a solved, boring problem. The actual hard part is layout. If your ingestion layer doesn't know that a bold header implies hierarchy, or that a two-column page isn't just one long string of text read left-to-right, your LLM is reading garbage.
Markdown won the ingestion war
We've mostly stopped treating PDFs as plain text. Markdown is now the default format for document ingestion, simply because it preserves structure.
Modern ingestion tools don't just dump strings. They output Markdown where headers, lists, and tables actually mean something. This gives the LLM the context it needs to figure out where a piece of information lived in the original document, which makes citations and retrieval significantly more accurate.
