Comment by disgruntledphd2

> So what use cases are those?

I think that as software/data people, we tend to underestimate the number of business processes that are repetitive but require natural language parsing to be done. Examples would include supply chain (basically run on excels and email). Traditionally, these were basically impossible to automate because reading free text emails and updating some system based on that was incredibly hard. LLMs make this much, much easier. This is a big opportunity for lots of companies in normal industries (there's lots of it in tech too).

More generally, LLMs are pretty good at document summarisation and question answering, so with some guardrails (proper context, maybe multiple LLM calls involved) this can save people a bunch of time.

Finally, they can be helpful for broad search queries, but this is much much trickier as you'd need to build decent context offline and use that, which (to put it mildly) is a non-trivial problem.

In the tech world, they are really helpful in writing one to throw away. If you have a few ideas, you can now spec them out and get sortof working code from an LLM which lowers the bar to getting feedback and seeing if the idea works. You really do have to throw it away though, which is now much, much cheaper with LLM technology.

I do think that if we could figure out context management better (which is basically decent internal search for a company) then there's a bunch of useful stuff that could be built, but context management is a really, really hard problem so that's not gonna happen any time soon.