Your RAG System Is Lying To You About That Table

Short summary

RAG systems struggle with tables because text chunking doesn't preserve positional meaning—headers get separated from data rows, footnotes become orphaned, and explanatory text placed far from tables stays disconnected. The 2026 REAL-MM-RAG benchmark empirically confirmed this gap: retrieval models fail consistently on table-heavy documents even when handling surrounding prose correctly. Researchers are exploring layout-aware chunking and vision-based approaches, but the problem remains unsolved for most implementations, especially on-demand systems without preprocessing.

• •Text-based chunking breaks table structure by separating headers from data rows
• •Footnotes and explanatory text become orphaned chunks disconnected from the tables they describe
• •REAL-MM-RAG benchmark confirms retrieval models systematically fail on table-heavy documents
• •Emerging solutions include layout-aware chunking and vision-based models, but remain early-stage

Generated with AI, which can make mistakes.