I have dealt with some codebases that were purely assembled with ML and they tend towards being completely unmaintainable nightmares exhibiting all the worst te

I have dealt with some codebases that were purely assembled with ML and they tend towards being completely unmaintainable nightmares exhibiting all the worst tenets of things like object-oriented code design. Levels of inheritance 8 modules deep, global variables being passed around everywhere to escape them, the works. For ML driven code development, I find it works best when I used it to make pure functions where I know exactly what I expect to go in and out of the function, and the LLM can simultaneously write the tests for it to ensure that it works. LLMs do not plan like humans, even when finetuned they seem to have difficulty being integrative with knowledge beyond pattern matching. That being said, 90% of coding is pattern matching to something someone made already. And as long as I'm writing pure functions and providing suitably adequate context for what the model needs to produce, LLMs seem to work wonders. My rule of thumb is to spend 10-20 minutes specifying exactly what I need in the prompt, and then tuning that if I fail to get the expected result.