[PoignardAzur]

I'm skeptical of this takeaway.

"I spent days trying to solve X by doing Y, then it turned out I could have solved it by doing Z instead" is an experience I've had countless times before LLMs were a thing. Sometimes you really do need these three days of stumbling before you can build up the confidence to do the easy solution.

(Then again, I don't know the specifics of your case.)

[danielbln]

In addition, I feel like you build a sort of intuition after a while and detect when the conversation with the LLM has hit a dead end. When to stop and take a step back, think what it is you're trying to do and try to go down a different path. The LLM can even support with that, it's on the human to kick that off, at least at this point in time.

[aconsult1]

I can't really disagree with you. Although I want to believe this problem is more exacerbated with LLMs than with human-tutors.

I have no evidence other than hundreds of hours using ChatGPT.

[PoignardAzur]

> Although I want to believe this problem is more exacerbated with LLMs than with human-tutors.

Well, yeah, but the central insight here is that LLMs enable a worse-is-better approach with a tighter feedback loop. They're not as good as a regular tutor, but a regular tutor costs money, gets impatient, is only available at set hours, etc.

Part of the appeal of learning-by-LLM is that you can get a flash of motivation at 2 AM and go "hey, I should totally learn about X, that would be cool!", open up ChatGPT, ask some very naive questions, and get just enough to get started.

[PoignardAzur]

(It's funny, when I wrote this yesterday I thought "this is an unrealistic example", and yet here I am, at 1 AM, asking ChatGPT a bunch of questions about Google's XLS. I'm pretty sure the answers I got were hallucinations, but at least it helped me formulate the questions for when I go to the mailing list.)