When it comes to traditional NLP related tasks, LLMs are far below dedicated NLP pipelines like POS tagging and feature tagging. However, fine tuning bridges the gap quite a bit between the two.

It's a narrow domain, but so is most of programming. I think if you're just training a general purpose LLM to be more inclined towards your data -- no, fine tuning is probably not very relevant. But if you're trying to solve a very specific yet fuzzy problem, and LLMs can get you _part_ of the way there, fine tuning is likely your best bet.