If you just throw every task at it, I can completely imagine that it’ll cost you more than any gain in efficiency. To some degree I think this is true of some coding tasks (not all, most are pretty efficient pattern matches, and that’s what it does well).
But using LLMs to build tools and pipelines that stand alone (no AU built into it) and enable human productivity seems like a far higher leverage use.
The programming version is building the libraries and abstractions that are robust and well tested, so that regular developers can quickly build and refine the features.
Or building the reporting dashboard. Or whatever.
The cost is only going to go up, and the companies that lock in some non-AI process improvements before the hike will likely be smiling
This is almost the perfect task for them. I think if them as pattern matches. They have patterns for libraries in their training, and you gave it a technical spec, and it pattern matched it across to a library.
On top of that, you can verify it and reuse it. But regenerating it every time wouldn’t be a good use both for the cost, and the risk of subtle issues that don’t get noticed. Same argument as for any library.
Probably because I’ve been doing this so long, I often find it easier and more precise to describe things in code or pseudo code than common English, which often aides my use of LLMs.
On another note, I’m curious what you’re making. My “when I get time” project is to use an old STM32 drone flight controller to do some basic robotics, which will only be possible because I think the LLM will pattern match me out of trouble getting an embedded C program compiling after 20 years out of that game.