People are really freaked out about hallucinations, but you can totally tackle that with solid prompts. The one in the repo right now is doing a pretty good job. Keep in mind though, this project is all about maxing out context for LLMs in products that need PDF input.

We're not talking about some hardcore archiving system for the Library of Congress here. The goal is to boost consistency whenever you're feeding PDF context into an LLM-powered tool. Appreciate the feedback, I'll be sure to add that in.

I messed around with some rotating tables in that Apollo 17 demo video - you can check it out in the repo if you want. It's pretty straightforward to tweak just by changing the prompt. You can customize that prompt section in the code to fit whatever you need.

Oh, and if you throw in a line about LaTeX, it'll make things even more consistent. Just add it to that markdown definition part I set up. Honestly, it'll probably work pretty well as is - should be way better than those clunky old OCR systems.

It is a lot cheaper! While cost-effectiveness may not be the primary advantage, this solution offers superior accuracy and consistency. Key benefits include precise table generation and output in easily editable markdown format.

Let's make some numbers game:

- Average token usage per image: ~1200 - Total tokens per page (including prompt): ~1500 - [GPT4o] Input token cost: $5 per million tokens - [GPT4o] Output token cost: $15 per million tokens

For 1000 documents: - Estimated total cost: $15

This represents excellent value considering the consistency and flexibility provided. For further cost optimization, consider:

1. Utilizing GPT4 mini: Reduces cost to approximately $8 per 1000 documents 2. Implementing batch API: Further reduces cost to around $4 per 1000 documents

I think it offers an optimal balance of affordability & reliability.

PS: One of the most affordable solution on market, cloudconvert charges ~30$ for 1K document (pdftron mode required 4 credits)