The trick to learning with LLMs is to treat them as one of multiple sources of information, and work with those sources to build your own robust mental of how things work.
If you exclusively rely on official documentation you'll miss out on things that the documentation doesn't cover.
WinAPI docs are pretty accurate and up to date
If the WinAPI docs are solid you can do things like copy and paste pages of them into Claude and ask a question, rather then manually scan through them looking for the answer yourself.
Apple's developer documentation is mostly awful - try finding out how to use the sips or sandbox-exec CLI tools for example. LLMs have unlocked those for me.
Unless you're just taking the LLM answers at face value?
That's always a better signal than anything that official documentation might tell you.
Does it have any threading preconditions? Any weird quirks? Any strange UB? That's stuff you can't find out just by testing. You can ask the LLM, but then you have to read the docs anyway to check its answer
I do a lot of astrophysics. Universally LLMs are wrong about nearly every astrophysics questions I've asked them - even the basic ones, in every model I've ever tested. Its terrifying that people take these at face value
For research at a PhD level, they have absolutely no idea what's going on. They just make up plausible sounding rubbish
It's curious how different people come to very different conclusions about the usefulness of LLMs.
Every time I ask LLMs questions I know the answers to, its results are incomplete, inaccurate, or just flat out wrong much of the time
The idea that AI is an order of magnitude superior to coders is flat out wrong as well. I don't know who he's talking to
Sure it's not there yet. Give it a few months
I asked chatgpt the other day:
"Where did elements heavier than iron come from?"
The answer it gave was totally wrong. Its not a hard question. I asked it this question again today, and some of it was right (!). This is such a low bar for basic questions