Also, all code is wrong in the wrong context, all code is right in the right context, the reason AI cannot one shot a complete architecture is that it's not a defined and possible task - if you fully specify the architecture the AI isn't designing anything, and if you don't fully specify the architecture how is the AI going to resolve ambiguity without either guessing, asking questions to make you do the necessary work, or refusing to work until it's fully specified?

AI is a stochastic process, it's more like finding the answer to a particular problem using simulated annealing, a genetic algorithm, or a constrained random walk. It's been trained on code well enough that there's a high density probability field around the kinds of code you might want, and that's what you see often - middle of the road solutions are easy to one shot.

But if you have very specific requirements, you're going to quickly run into areas of the probability cloud that are less likely, some so unlikely that the AI has no training data to guide it, at which point it's no better than generating random characters constrained by the syntax of the language unless you can otherwise constrain the output with some sort of inline feedback mechanism (LSP, test, compiler loops, linters, fuzzers, prop testing, manual QA, etc etc).