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this is a very important one IME. maintaining (and snooping on) my RAG slackbot for the last couple years has shown me people have a wide range of intuitions for what it “ought” to be able to help with. most expect too much

Btw. I appreciate your rebuttal here and don’t dispute you have some good points

They're still LLMs with roughly the same capabilities, so the most important lessons about what problems are a good fit for LLMs and what problems aren't tend to hold steady

My thoughts on this: LLMs are not testimonial sources. We split fact-checking responsibilities with testimonial sources: we can't do that here. It's a pattern matcher, and not all real patterns are real things. Appropriate problems are ones where this status is OK. Not an answer, but a start?

Another one is knowing in which domains you can reliably identify errors. There's a level of dispassionate self awareness that's really important when working with them.

genuine question - where would the eventual line between the ai equivalents of "being good at excel idiosyncrasies" and "being good at data science" be drawn?

much like vb macros the vendor specific stuff is usually trash, also

The fundamental challenge here is that there are key skills - reading, writing, thinking - that students urgently need to develop The temptation to outsource those activities to generative AI will clearly harm the acquisition of those skills - they need to be a slog

I do think that generally, the more skillfully one uses GenAI, the more interesting are the artifacts that emerge downstream. And staying on top of what systems & capabilities are available takes some elbow grease. Even if you have an ai doing *that* for you, can’t imagine it being a “set & forget”