this post was submitted on 07 Jul 2025
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I'm in a workplace that has tried not to be overbearing about AI, but has encouraged us to use them for coding.
I've tried to give mine some very simple tasks like writing a unit test just for the constructor of a class to verify current behavior, and it generates output that's both wrong and doesn't verify anything.
I'm aware it sometimes gets better with more intricate, specific instructions, and that I can offer it further corrections, but at that point it's not even saving time. I would do this with a human in the hopes that they would continue to retain the knowledge, but I don't even have hopes for AI to apply those lessons in new contexts. In a way, it's been a sigh of relief to realize just like Dotcom, just like 3D TVs, just like home smart assistants, it is a bubble.
The first half dozen times I tried AI for code, across the past year or so, it failed pretty much as you describe.
Finally, I hit on some things it can do. For me: keeping the instructions more general, not specifying certain libraries for instance, was the key to getting something that actually does something. Also, if it doesn't show you the whole program, get it to show you the whole thing, and make it fix its own mistakes so you can build on working code with later requests.
I've had good results being very specific, like "Generate some python 3 code for me that converts X to Y, recursively through all subdirectories, and converts the files in place."
I have been more successful with baby steps like: "Write a python 3 program that converts X to Y." Tweak prompt until that's working as desired, then: "make it work recursively through all subdirectories" - and again tweak with specifics like converting the files in place, etc. Always very specific, also - force it to fix its own bugs so you can move forward with a clean example as you add complexity. Complexity seems to cap out at a couple of pages of code, at which point "Ooops, something went wrong."