This is my experience, too. As a concrete example, I'll need to write a mapper function to convert between a protobuf type and Go type. The types are mirror reflections of each other, and I feed the complete APIs of both in my prompt.
I've yet to find an LLM that can reliability generate mapping code between proto.Foo{ID string} to gomodel.Foo{ID string}.
It still saves me time, because even 50% accuracy is still half that I don't have to write myself.
But it makes me feel like I'm taking crazy pills whenever I read about AI hype. I'm open to the idea that I'm prompting wrong, need a better workflow, etc. But I'm not a luddite, I've "reached up and put in the work" and am always trying to learn new tools.
An LLM ability to do a task is roughly correlated to the number of times that task has been done on the internet before. If you want to see the hype version, you need to write a todo web app in typescript or similar. So it's probably not something you can fix with prompts, but having a model with more focus on relevant training data might help.
This honestly seems like something that could be better handled with pre-LLM technology, like a 15-line Perl script that reads one on stdin, applies some crufty regexes, and writes the other to stdout. Are there complexities I'm not seeing?
I've yet to find an LLM that can reliability generate mapping code between proto.Foo{ID string} to gomodel.Foo{ID string}.
It still saves me time, because even 50% accuracy is still half that I don't have to write myself.
But it makes me feel like I'm taking crazy pills whenever I read about AI hype. I'm open to the idea that I'm prompting wrong, need a better workflow, etc. But I'm not a luddite, I've "reached up and put in the work" and am always trying to learn new tools.