Mind-boggling idea to do this because OCR and pulling info out of PDFs has been done better and for longer by so many more mature methods than having an LLM do it
I don’t think this idea is totally cursed, I think the implementation is. Instead of using it to shortcut filling in grades that the applicant could spot check, like a resume scraper, they are just taking the first pass from the LLM as gospel.
If all the PDFs are the same format you can use plenty of existing techniques. If you have no control at all over that format you're in for a much harder time, and vLLMs look perilously close to being a great solution.
Just not the GPT-5 series! My experiments so far put Gemini 2.5 at the top of the pack, to the point where I'd almost trust it for some tasks - but definitely not for something as critical as extracting medical grades that influence people's ongoing careers!
I have come to the same conclusion having built a workflow that has seen 10 million+ non-standardized PDFs (freight bill of ladings) with running evaluations, as well as against the initial "ground-truth" dataset of 1,000 PDFs.
Humans: ~65% accurate
Gemini 1.5: ~72% accurate
Gemini 2.0: ~88% accurate
Gemini 2.5: ~92%* accurate
*Funny enough we were getting a consistent 2% improvement with 2.5 over 2.0 (90% versus 88%) until as a lark we decided to just copy the same prompt 10x. Squeezed 2% more out of that one :D
Got it. The non-experts are holding it wrong!
The laymen are told "just use the app" or "just use the website". No need to worry about API keys or routers or wrapper scripts that way!
Sure.
Yet the laymen are expected to maintain a mental model of the failure modes and intended applications of Grok 4 vs Grok 4 Fast vs Gemini 2.5 Pro vs GPT-4.1 Mini vs GPT-5 vs Claude Sonnet 4.5...
It's a moving target. The laymen read the marketing puffery around each new model release and think the newest model is even more capable.
"This model sounds awesome. OpenAI does it again! Surely it can OCR my invoice PDFs this time!"
I mean, look at it:
GPT‑5 not only outperforms previous models on benchmarks and answers questions more quickly, but—most importantly—is more useful for real-world queries.
GPT‑5 is our best model yet for health-related questions, empowering users to be informed about and advocate for their health. The model scores significantly higher than any previous model on HealthBench , an evaluation we published earlier this year based on realistic scenarios and physician-defined criteria.
GPT‑5 is much smarter across the board, as reflected by its performance on academic and human-evaluated benchmarks, particularly in math, coding, visual perception, and health. It sets a new state of the art across math (94.6% on AIME 2025 without tools), real-world coding (74.9% on SWE-bench Verified, 88% on Aider Polyglot), multimodal understanding (84.2% on MMMU), and health (46.2% on HealthBench Hard)
The model excels across a range of multimodal benchmarks, spanning visual, video-based, spatial, and scientific reasoning. Stronger multimodal performance means ChatGPT can reason more accurately over images and other non-text inputs—whether that’s interpreting a chart, summarizing a photo of a presentation, or answering questions about a diagram.
We aren't talking about non-experts here. Go read https://www.thalamusgme.com/blogs/methodology-for-creation-a...
They're clearly competent developers (despite mis-identifying GPT-5-mini as GPT-5o-mini) - but they also don't appear to have evaluated the alternative models, presumably because of this bit:
"This solution was selected given Thalamus utilizes Microsoft Azure for cloud hosting and has an enterprise agreement with them, as well as with OpenAI, which improves overall data and model security"
I agree with your general point though. I've been a pretty consistent voice in saying that this stuff is extremely difficult to use.
The solution architect, leads, product managers and engineers that were behind this feature are now laymen who shouldn't do their due diligence on a system to be used to do an extremely important task? They shouldn't test this system across a wide range of input pdfs for accuracy and accept nothing below 100%?
Like, this company could have done the same projects we've been doing but probably gotten them done faster (and certainly with better performance and lower operational costs) any time in the last 15 years or so. We're doing them now because "we gotta do 'AI'!" so there's funding for it, but they could have just spent less money doing it with OpenCV or whatever years and years ago.
If you have a better way to parse PDFs using opencv or whatever, please provide this service and people will buy it for their RAG chat bots or to train vlms.
> that information is buried in PDFs sent by schools (often not standardized).
I don't think OCR will help you there.
An LLM can help, but _trusting_ it is irresponsible. Use it to help a human quickly find the grade in the PDF, don't expect it to always get it right.
The particular challenge here I think is that the PDFs are coming in any flavor and format (including scans of paper) and so you can't know where the grades are going to be or what they'll look like ahead of time. For this I can't think of any mature solutions.
Edit: Does sound like it - "Cortex uses automated extraction (optical character recognition (OCR) and natural language processing (NLP)) to parse clerkship grades from medical school transcripts."
But... that document also says:
"For machine-readable transcripts, text was directly parsed and normalized without modification. For non-machine-readable transcripts, advanced Optical Character Recognition (OCR) powered by a Large Language Model (LLM) was applied to convert unstructured image-based data into text"
Which makes it sounds like they were using vision-LLMs for that OCR step.
Using a separate OCR step before the LLMs is a lot harder when you are dealing with weird table layouts in the documents, which traditional OCR has usually had trouble with. Current vision LLMs are notably good at that kind of data extraction.