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Notes -
I've built multiple LLM based projects that run in production so I might have some answers. Basically all automation of really repetitive but simple cognitive work. Don't expect the LLM to do all for you, use some prompt engineering, some regex, some external ML, some programming to glue it all together.
If you're a programmer it's use cases are quite obvious. It can write code for you. But also you can do NLP tasks from your wildest dreams. Clean extremely messy and inconsistent user data that no regex in the world could solve? done. Create a semantic search engine in 5 minutes? Done. Anything NLP application based is just a solved problem now.
If you want to run a local LLM go see what /r/LocalLLama recommends and or what's popular on hugging face right now. Even though I would say that if you are asking this question at the end of 2023 you probably have been living under a cave and should learn basic programing first before you have a shot at training your own LLMs.
I suggest you get a feel for openais llms first in the playground and see where your mind takes you. Talk to gpt and use it as a search engine and you will get a rough idea of it's capabilities.
It’s interesting how useful LLMs seem for programming. I guess I shouldn’t be surprised that the first thing software engineers optimized their new toy to do was write code. It’s like how there are so many moves about movies.
I think also software engineers are especially attuned to inefficiencies in productivity in a way regular white-collar workers are blind/indifferent to. Software engineers imo are 10-100x more efficient at doing the same work than even other marginally technical white-collar workers like data analysts do (I'm sure there is a lot of productivity in just knowing how to use concurrent/asynchronous programming, on top of programming at all).
Software devs are the only people increasing their wpm, installing window mangers, use the cli, makes custom keyboard shortcuts, leveraging scripts and automation whenever feasible (sometimes to their detriment) etc.
You see a lot of efficiency exploration in finance, consulting, and some doctors. The last field is mine and you see a lot of people desperately trying to figure out how to apply LLMs to save time or increase throughput, and I've heard tons of stories about finance and consulting people doing similar things prior to LLMs.
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