This presumes an LLM has something to say. The reason the text doesnt make much sense, is because it doesnt. It isnt "on topic" because it has no topic to be on. Sentences go on, are joined irrelevantly, and so on because theyre sampled from a distribution of "possibly relevant sentences". They aren't being addressed to a reader (whose mind is being modelled), on a topic the writer wishes to express.
Communication expresses an understanding. LLMs arent communicating.
LLMs generate code well enough. Code has no natural language semantics. Programming's condition of satisfaction is that the machine executes correctly. The condition of satisfaction of communication is that the communication expresses what there is to be communicated.
In the limit where an LLM is trained to repoduce an exmplar of communication, it seems as if it does so. In that limit, LLMs can reporduce anything.
If frontier labs wanted to improve "LLMs for communication" they would pay for reasoning traces to be produced which reproduced the thought process of writers/speakers thinking about their audiences. Rarely do such traces exist in prior datasets .
What I find they do badly in code is building abstraction and breaking the problem down in a way a human does. They don't seem to really build simplifying components unless you expressly tell them too, they are more than happy to just nest 10 deep or embed a 20 line called back function and use lots of shortened names. I find I can improve the code just by organising better and choosing some abstractions that make sense to the humans and then ironically you save tokens too because the AI will reuse it, but it wont create most of it itself. Its bad for domain work unless you are explicit about it.