[dabs]

Good question!

ChatGPT certainly makes it easier to implement a prototype that works quite well for documents meeting certain conditions. For example, it does pretty well with restaurant menus out of the box because the entities extracted tend to have fairly unique text.

However, with documents where you have a lot of repetition or complex tabular structures even the latest ChatGPT isn't enough. It struggles capturing the structure of the table in the output, and struggles when the same text appears in different instances in the document.

This is where a hybrid system that merges the zero-shot strenghts of ChatGPT, but that also leverages strong priors and conditioning from strong heuristics, can yield a much better end product.

Currently the implementation is more of the LLM heavy side, but our plan is to iterate to include more of these heuristics to get a more robust tool overall across different document types.