Well, what would I have to do to please the LLM? Writing code isn't for LLMs to consume but rather to communicate intent for people and for machines to run which provides value for the user at the end of the day. If an LLM fails at being useful within a codebase when it's supposed to be a "works anywhere" tool then the tool is less than useful.
Note that language servers, static analysis tooling, and so on still work without issue.
The cause (which is my assumption) is that there aren't enough good examples in the training set for anything useful to be the most likely continuation thus leading to a suboptimal result given the domain. Thus the tool doesn't work "everywhere" for cases where there's less use of a language or less code in general dealing with a particular problem.
Note that language servers, static analysis tooling, and so on still work without issue.
The cause (which is my assumption) is that there aren't enough good examples in the training set for anything useful to be the most likely continuation thus leading to a suboptimal result given the domain. Thus the tool doesn't work "everywhere" for cases where there's less use of a language or less code in general dealing with a particular problem.