AWS Textract does use ML and I’ve personally used it to parse tables for automated invoice processing.

You wouldn’t get a markdown document automatically generated (or at least you couldn’t when I last used it a few years ago) but you did get an XML document

That XML document was actually better for our purposes because it gives you a confidence score and is properly structured, so floating frame, tables and columns would be properly structured in the output document. This reduces the risk of hallucinations.

It’s less of an out-of-the-box solution but that’s to be expected with AWS APIs.

> you might get some skipped bits, but that's not a dealbreaker for folks looking to feed PDFs into AI systems

Unless it is. We have a few hundred PDF per month (mostly tables) where we need 100% accuracy. Currently we feed them into an OCR and have humans check the result. I do not win anything if I have to check the LLM output, too.

You and your delegated writing are lying about cost.

It is off by 2 orders of magnitude.

My guess is you're using the token counting algorithm for pre-4o with the costs for 4o and later.

That aside, I strongly suggest taking a week off from code-outside-work and use that time to reflect-as-work. The post and ensuing comments are a horror show. Don't take it too hard, it probably won't matter in the long run, no ones going to remember.

But you'd get a lot out of taking it harder than you did in the comments I've seen, including one this morning where you replied to me. It worries me that you don't seem to understand how sloppy this work is.

When I was 14, my math teacher gave me a 0 on a test because I just wrote the answers instead of showing work. That gave me a powerful appreciation for being precise, clear, and accurate.

The only positive outcome is that even though there was enough upvotes for a simple, sloppy, mispurposed GPT wrapper to end up on the front page for ~16 hours, near-universally, the comments seem to understand contextually there's a lot of problems with how this was shared.

I will say I have had a look at your code here. I really do value your innovation here in gaining better accuracy, but I don't think it's is much more accurate for obscure PDF cases - Maybe it halves those obscure errors. I found it still hallucinated or failed to parse some text (e.g. that unusual languages, screenshots with tiny blurred JPEG text, images/shapes remain hallucination issues with your solution). BTW I noticed a small typo "Convert document as is be creative to use markdown effectively" in the prompt. For me changing this and adding text about returning "None" if the text is unreadable reduced hallucinations.

Would you contrast your accuracy with Textract? Because Textract is 10x cheaper than this at approx 1 cent per page (and 20x cheaper than Cloudconvert). What documents make more sense to use with your tool? Is it worth waiting till gpt-4o costs drop 10x with the same quality level (i.e. not gpt-4o-mini) to use this? In my use case it's better to drop than to hallucinate.

What do you think makes sense in relation to Textract?

That is not a tricky example. Those tables are as clear cut as clear cut can be.