They're amazing at it, provided you keep asking the right questions.

Trust me when I say that in the hands of someone who doesn't have your experience, the LLMs would not be getting the results you get.

You might think what you're doing is trivial, it may be sessions that flow roughly, "Instrument this, okay this part is slow, profile this part, OK read the profile output and suggest a better approach".

But your experience will be steering it in the right direction, and you're probably unaware of just how much your experience is doing that guiding, as the LLM shoots off at 100mph, you feel like it's taking you with it, but you will be guiding it a lot more than you realise, and that's where learning and experience comes in, even if you're no longer operating at the lowest depth, your knowledge of that layer will be helping.

If nothing else, the experience to know when something is actually slow is a skill in itself. If a function takes 200ms, sometimes that's as quick as it can realistically go, and sometimes that's literally a million times slower than it could be, and there's actual skill and experience wrapped up in knowing what "slow" looks like.

So what should newcomers do who haven't yet gained enough expertise to ask LLMs the right questions?

“asking the right questions” is built on years of experience doing the things now being offloaded to AI

“asking the right questions” is also a moving target with each model release

People simply underestimate the value of doing the work and think that the end result is all that matters