You may need to be very specific about how you prompt to get decent results in a particular style. Any wiggle room for ambiguity and the bots'll drive a bulldozer through what you meant with insane interpretations.
I don't use cloud based LLMs at all -- local only -- so I'm not sure what they give you to work with there, but in my own usage, setting the system prompt with clearly defined ROLE, CONSTRAINTS, and other other details has been critical for getting them to do anything useful instead of spitting out generic analysis slop. (Even so, be mindful of context limits.)
LLMs aren't magic and don't have infinite memory. In fact, the amount they can hold in their heads effectively is pretty small and you must manage context carefully if you don't want them to get lost going down irrelevant paths.
What they are very good at is style transfer. If you have text and need it reworded, they can do that trivially.
They are not very good at dealing with large structural reasoning. You really have to break things down and hand-hold them if you want to get them to do stuff like that.
I haven't done much directly with creative writing in natural language (I've mostly done code gen, critical news analysis, image analysis, and a lot of context experimentation) -- but the path I'd try out if I wanted to get into that is actually using a lot of writing "self-help" strategies. e.g. Snowflake Method. Lots of outlines. Character profile drafting. Scene and sequel. Apply Vonnegut's rules for writing. Etc. Then set up multiple LLMs as test readers tuned to different aspects of the writing that you want -- like character consistency, diction, appeal to your audience, etc. and have them review it and iterate.