Not the OP, but you can take a look at https://spacy.io/usage/spacy-101 to get a sense of what traditional NLP tasks look like. These things can be done much faster than LLMs with appropriate tooling (such as spaCy) and they don’t risk hallucination.

I’ve tried PII anonymization with standard NLP approaches and LLMs have been (way) better at this task in my experience.

Also you can do it at scale with SparkNLP: https://sparknlp.org

Many of the use cases I've seen for LLMs would actually be better with NLP.

Which kinds of use cases?

In this comment I was referring to POS tagging and feature extraction.

Another use case for fine tuning we have as well is to reduce a 5-shot prompt that we have to run hundreds of times per request down to a “0-shot” (heavy emphasis on the double quotes.

Run five shot on gpt-4o a couple thousand times, then fine tune on cohere’s command-r or haiku or llama3 8b or whichever small but mighty llm. You can reduce costs by 99%, or somewhere in that ballpark, without really sacrificing quality on 99% of the queries.