In the Linux kernel I've had a lot of luck with:

1. "find the code that does X"

2. go read that code

3. When you hit a bit you don't care about, go back to the model and ask it for the pertinent details

4. When you hit a really confusing bit, ask the model for hypotheses about what's going on. (I always phrase it as "give me some hypotheses" not "what is going on here". I dunno if this changes the output but I think it helps me stay in a mindset of uncertainty, it's important to avoid locking in any misunderstandings. Anyway I find the models do well at this task, and when they bullshit here it has a strong smell).

Before AI, parts 1 and 3 could be insanely time consuming, sometimes it felt like a infinite breadth-first-search. And part 4 was basically: either you find a human who knows the code, or you just make a mental note and hope that later on you find something that makes you go "oh, THAT'S why they <do weird thing that should 100% have a comment>!".

So yeah even though you're still reading code with your wetware the AI makes you dramatically more powerful.

This is also extremely helpful for unpicking undocumented API contracts. E.g. you can say "the x86 implementation of this API is safe to call under a spinlock, go read the other arch versions and tell me if that's true there too".