[72deluxe]

This seems to be the issue with using LLMs for any code generation. Even with my own code bases that I've written entirely by hand over years, if I use AI to implement anything, I don't go through the mental model of architecting it, so I don't know how it works. I can only imagine this to be far, far worse for large code bases maintained by a team of people who are all using AI.

[win311fwg]

That depends on the language you are using. Some language communities had already rejected "architecture astronauts" before LLMs were born, so the training data was highly consistent, which has lead to LLMs being highly consistent in their output. You know how it works because the LLM spits out the same as what you would have written yourself. It's almost eerie that they can do that.

Unfortunately that doesn't apply to all languages. LLMs are especially bad at producing code for the languages that were historically known as beginner-friendly as the training data was full of code by beginners doing what beginners do. All bets are off if you get stuck there. (Although maybe you could use an LLM to translate your code to a language that LLMs are good at!)

[72deluxe]

Very intriguing. I write C++ which definitely isn't considered beginner-friendly. Yet because of the large amount of code a LLM can churn out, I mentally don't recall what it does, like making a working car but no idea of how the engine works. Perhaps I need to spend more time reading its code output, but then I might as well write it myself...

[BadBadJellyBean]

One problem there is that even if the code is consistent, idiomatic Rust is not the same as idiomatic C.

So either the translation produced Rust code in the "shape" of C code or the code is quite different from the C implementation.

Add to that that you need to have people proficient in Rust and also Postgres as well as the very much unknown codebase as a whole you get a recipe for pain.