It's mostly a statistical text model, although the RL "reasoning" stuff added in the past 12 months makes that a slightly less true statement - it has extra tricks now to help it bias bits of code to statistically predict that are more likely to work.

The real unlock though is the coding agent harnesses. It doesn't matter any more if it statistically predicts junk code that doesn't compile, because it will see the compiler error and fix it. If you tell it "use red/green TDD" it will write the tests first, then spot when the code fails to pass them and fix that too.

> How are they not seeing the propensity for both Claude + ChatGPT to spit out tons of completely pointless error handling code, making what should be a 5 line function a 50 line one?

TDD helps there a lot - it makes it less likely the model will spit out lines of code that are never executed.

> How are they not seeing that you constantly have to nag it to use modern syntax. Typescript, C#, Python, doesn't matter what you're writing in, it will regularly spit out code patterns that are 10 years out of date.

I find that if I use it in a codebase with modern syntax it will stick to that syntax. A prompting trick I use a lot is "git clone org/repo into /tmp and look at that for inspiration" - that way even a fresh codebase will be able to follow some good conventions from the start.

Plus the moment I see it write code in a style I don't like I tell it what I like instead.

> And that's not even including the bit where the AI obviously decided to edit the wrong search function in a totally different part of the codebase that had nothing to do with what my colleague was doing.

I usually tell it which part of the codebase to execute - or if it decides itself I spot that and tell it that it did the wrong thing - or discard the session entirely and start again with a better prompt.