Ingesting PDFs and why Gemini 2.0 changes everything

I work in fintech and we replaced an OCR vendor with Gemini at work for ingesting some PDFs. After trial and error with different models Gemini won because it was so darn easy to use and it worked with minimal effort. I think one shouldn't underestimate that multi-modal, large context window model in terms of ease-of-use. Ironically this vendor is the best known and most successful vendor for OCR'ing this specific type of PDF but many of our requests failed over to their human-in-the-loop process. Despite it not being their specialization switching to Gemini was a no-brainer after our testing. Processing time went from something like 12 minutes on average to 6s on average, accuracy was like 96% of that of the vendor and price was significantly cheaper. For the 4% inaccuracies a lot of them are things like the text "LLC" handwritten would get OCR'd as "IIC" which I would say is somewhat "fair". We probably could improve our prompt to clean up this data even further. Our prompt is currently very simple: "OCR this PDF into this format as specified by this json schema" and didn't require some fancy "prompt engineering" to contort out a result.

Gemini developer experience was stupidly easy. Easy to add a file "part" to a prompt. Easy to focus on the main problem with weirdly high context window. Multi-modal so it handles a lot of issues for you (PDF image vs. PDF with data), etc. I can recommend it for the use case presented in this blog (ignoring the bounding boxes part)!

How do today’s LLM’s like Gemini compare with the Document Understanding services google/aws/azure have offered for a few years, particularly when dealing with known forms? I think Google’s is Document AI.

I've found the highest accuracy solution is to OCR with one of the dedicated models then feed that text and the original image into an LLM with a prompt like:

"Correct errors in this OCR transcription".

How does it behave if the body of text is offensive or what if it is talking about a recipe to purify UF-6 gas at home? Will it stop doing what it is doing and enter lecturing mode?

I am asking not to be cynical but because of my limited experience with using LLMs for any task that may operate on offensive or unknown input seems to get triggered by all sorts of unpredictable moral judgements and dragged into generating not the output I wanted, at all.

If I am asking this black box to give me a JSON output containing keywords for a certain text, if it happens to be offensive, it refuses to do that.

How does one tackle with that?