Comment by nobleach

I think I can get on board with this view. In the earlier LLM days, I was working on a project that had me building models of different CSV's we'd receive from clients. I needed to build classes that represented all the fields. I asked AI to do it for me and I was very pleased with the results. It saved me an hour-long slog of copying the header rows, pasting into a class, ensuring that everything was camel-cased, etc. But the key thing here is that that work was never going to be the "hard part". That was the slog. The real dopamine hit was from solving the actual problem at hand - parsing many different variants of a file, and unifying the output in a homogenous way.

Now, if I had just said, "Dear Claude, make it so I can read files from any client and figure out how to represent the results in the same way, no matter what the input is". I can agree, I _might_ be stepping into "you're not gonna understand the software"-land. That's where responsibility comes into play. Reading the code that's produced is vital. I however, am still not at the point where I'm giivng feature work to LLMs. I make a plan for what I want to do, and give the mundane stuff to the AI.