Building RAG (Retrieval-Augmented Generation) pipelines is a great way to supercharge LLMs with custom data. However, if your pipeline relies on parsing standard PDFs, you've probably hit a massive roadblock: table text duplication.
Most open-source PDF parsers extract table data twice. First, they extract it as a messy, misaligned block of standard prose text. Then, they extract the raw strings from the table cells.
This behavior completely destroys the LLM's understanding of the document layout and inflates your token usage by 3x or 4x.
Here is how I solved this issue in Python, and how you can implement the same logic in your data pipelines.
The Strategy: Bounding-Box Masking
