Hey everyone! 
I’ve been experimenting with the Assistants API and was wondering how others are integrating it into real-world applications or internal tools.
Are you using it for customer support, internal knowledge bots, product onboarding, or something totally different?
Also curious how you’re handling file uploads and memory — especially when building multi-step or context-heavy workflows.
Would love to hear your use cases or any lessons you’ve learned so far!
Short answer is yes. It takes away complexity of model, storage, memory, files etc all in one place.
I’d say that you can use it pretty much for all of the use cases that you mentioned.
For context heavy and file uploads, Assistants API make it easier than ever to handle them.
Do you have a more specific question, or a challenge?
Thanks! That helps a lot. I’m curious — have you tried feeding it big PDFs like medical reports? I’m hoping it handles those better than a panicked intern on day one 
Any tips for juggling multiple files or context-heavy tasks?
Heavy files. Yes.
Cannot be more than 512MB though. But medical reports should be fine.
So, one of the ways I ensure that its reading the right information is to see the data it references.
Eg:
Sometimes it refers to multiple files, and in my testing, I ensure that its right.
Other things you could do:
The prompting is still critical, as well as your desired output pathway.
If your just trying to feed in medical reports and get a natural language response - NBD - note that the “file size limit” is not what’s relevant, what’s relevant is the size of the context window compared to the model you’ve selected, and the amount of output you want.
If you are processing a 20 page report and looking for more than a 500-1000 word response (maximum if your lucky most of the time), you’re going to have to start chunking and defining data-mapping output schemas and providing those as input as a part of your prompt or using advanced function/tool calling schematics within the API.
If your trying to process a 2 page report and extract 10 “fill-in-the-blank” data points, and have that data be automatically saved to an output file or DB… and then automate the whole process of batch-input a stack of 50 reports, get all the output in the DB, do a second run to “check it”, compare the results, then run analytics on the final DB set, produce a call to notify the supervisor to review the results…
It’s all possible.
It takes a lot of code and patience and logical thinking - the LLM can supply the code, but not the patience or logical thinking or blueprinting system design.
The best place to start is: defining exactly what you want to do. What’s your inputs, what’s your outputs, what’s the process? You can mimic a human process - you can really do damn near anything with the LLM - but only within the limits of what you’ve logically mapped out architecturally, and who’s implementing it…
Like you can’t hire the LLM to be the job site super, or the architect, but they can definitely be the laborer that drives in the nails and makes the cuts.
