I started a greenfield project the other day and was excited to see how far AI has gotten for a real internal business use-case - not just a toy project. It w

I started a greenfield project the other day and was excited to see how far AI has gotten for a real internal business use-case - not just a toy project. It was great at setting up the scaffolding and helping with some of the tests but overall it might have been a drain on productivity.. I had to repeatedly correct it and explicitly tell it how to do things (or hunt down examples in the codebase) or it would make up APIs or generate not particularly idiomatic code for our codebase (was writing golang fwiw). There is really a balance between spoon-feeding it the answers vs just abandoning it - the spikiness is really apparent where it can totally nail some things but then utterly fail what should be pretty simple (i.e there was a point where I wanted to refactor two similar functions into a shared library and it just couldn't do it)