You're spot on about the challenges LLMs face with complex markdown tables, especially when column counts rise and data types are similar. The "counting task" f

You're spot on about the challenges LLMs face with complex markdown tables, especially when column counts rise and data types are similar. The "counting task" for column correlation is a real pain point – it's like the LLM loses track of where it is in the data grid. Your ID/coordinate marker idea is clever! It provides explicit context that LLMs seem to crave. Using HTML comments for this metadata is an interesting approach. It keeps the markdown valid for human readability, but I share your uncertainty about how consistently LLMs would parse and utilize it. Some other avenues worth exploring: Alternative Formats: Have you experimented with formats like CSV or JSON for feeding tabular data to LLMs? They might offer a more structured representation that's easier to parse. Pre-processing: Could we pre-process the table to create a more LLM-friendly representation? For example, converting it into a list of dictionaries, where each dictionary represents a row and keys represent column names. Prompt Engineering: Perhaps there are specific prompts or instructions that can guide LLMs to better handle large tables within markdown. It seems like there's room for innovation in how we bridge the gap between human-readable markdown tables and the structured data LLMs thrive on.