Even though I think most people know this deep down, I still don't think we actively realize how optimized LLMs are towards sounding good. It's the ultra processed food version of information consumption. People are super lazy (economical if you like) and rlhf et al have optimized LLM output to being easy to digest.

Consequence is you get a bunch of output that looks really good as long as you don't think about it (and they actively promotes not thinking about it) that you don't really understand, and that if you did dig into you'd realize is empty fluff or actively wrong.

It's worse than not learning, it's actively generating unthinking but palatable garbage that's the opposite of learning.

Yeah, you have to be really careful about how you use LLMs. I've been finding it very useful to use them as teachers, or to use them in the same way that I'd use a coworker. "What's the idiomatic ways to write this python comprehension in javascript?" Or, "Hey, do you remember what you call it when..." And when I request these things I'll try to ask in the most generic way possible so that I then get retype the relevant code, filling in the blanks with my own values.

That's just one use though. The other is treating it like it's a jr developer, which has its own shift in thinking. Practice in writing details specs goes a long way here.

>>Learning comes from grinding

Says who? While “grinding” is one way to learn something, asking AI for a detailed explanation and actually consuming that knowledge with the intent to learn (rather than just copy and pasting) is another way.

Yes, you should be on guard since a lot of what it says can be false, but it’s still a great tool to help you learn something. It doesn’t completely replace technical blogs, books, and hard earned experience, but let’s not pretend that LLMs, when used appropriately, don’t provide an educational benefit.