Au contraire - LLMs are quite bad at large scale pattern fidelity. They'll even forget key details and constraints unless told over and over again. That's why AI-written code has the quality of a patch-on-patch-on-patch.

Fully agree. I tried to refactor parts of a large code base with Fable+ultracode and it just keeps accidentally merging distinct concepts and making up explanations/reasonings that the code base did not contain.

For example, the code base contains a physical controller. It’s closed loop in that it can react in realtime to changes. But it’s a slightly untypical implementation because this one can even look into the future through simulations. But Fable does not understand that. Instead, I need to remind it every 30 minutes that this is closed loop. It keeps wrongly claiming that the controller was open loop and then based upon that it will make up constraints that don’t actually exist.

The patch-on-patch-on-patch is exactly right, nice way to describe it. It feels like, and I think is, optimized to find the quickest answer not necessarily the right answer.

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"It is not LLMs fault but you not knowing how to write a prompt". I know I know. But just giving all codebase and saying "rewrite it" is a no go. If e.g. going one class after class LLM will be exceptionally good at keeping the patterns and logics.

I mean it is a tool and you need to understand how the tool works. When there is too little context, where there is so much context so that you are poisoning it, when you are allowing the tool to do patch-on-patch and etc.

That's also true for humans.