When faced with jumbled data from messy documents, LLMs don't error out. Instead, they use their reasoning to guess, creating perfectly structured but factually wrong outputs. This silent data corruption is the most dangerous failure mode in production pipelines, as it pollutes downstream systems without warning.
To make AI agents reliable with messy data, strip the layout-parsing responsibility from the LLM. Build a defensive, code-based wrapper that pre-checks document quality, pre-structures the data using coordinates, and uses separate validation steps. This treats the LLM as a reasoner, not a parser.
Teams often try to fix data extraction errors by adding complex instructions to prompts. This fails because the root cause is a structural data engineering problem, not a semantic one. The LLM receives scrambled text tokens before it can even process the prompt's instructions, making the effort futile.