Comment by throwup238

> It is not perfect (if a search surfaces 300 promising documents, it will not check all of them, and it often misunderstands things due to lacking further context)

This has been the biggest problem for me too. I jokingly call it the LLM halting problem because it never knows the proper time to stop working on something, finishing way too fast without going through each item in the list. That’s why I’ve been doing my own custom orchestration, drip feeding it results with a mix of summarization and content extraction to keep the context from different documents chained together.

Especially working with unindexed content like colonial documents where I’m searching through thousands of pages spread (as JPEGs) over hundreds of documents for a single one that’s relevant to my research, but there are latent mentions of a name that ties them all together (like a minor member of an expedition giving relevant testimony in an unrelated case). It turns into a messy web of named entity recognition and a bunch of more classical NLU tasks, except done with an LLM because I’m lazy.