[schnitzelstoat]

I think most use-cases will still use simpler models like XGBoost etc. rather than LLM's. Customer segmentation is a really common use-case with no need for an LLM. Same for revenue/LTV forecasting.

Perhaps they can use the LLM to write and deploy these models without needing a Data Scientist but that seems risky to say the least.

In my company, the most Data Scientist-adjacent people are the Data Analysts but they tend not to have programming experience beyond SQL and basic Python and they aren't used to using the terminal etc.

[valzam]

Do those use cases need LLMs? Probably not. but if good results can be had with a day of prompting (in addition to the stuff mentioned in the article, which you have to do anyway) and a smaller model like Haiku gives good results why would you build a classifer before you have literally millions of customers?

The LLM solution will be much more flexible because prompts can change more easily than training data and input tokens are cheap.

[fn-mote]

> Do those use cases need LLMs? Probably not.

One of the points of the article is the importance of gathering data to support your conclusions.

> prompts can change more easily than training data

Training data is real, and prompts are not. I don’t think this is an apples to apples comparison.

[valzam]

I don't disagree that very numerical tasks like revenue forecasting are not a good fit for LLMs. But neither did a lot of data scientist concerns themselves with such things (compared to business analysts and the like). Software to achieve this has been commoditized.