From the article itself (and it’s just one of many use cases it mentions)

- Here’s when it’s the perfect choice: You have a high-volume, well-defined task. Ideal for functions like sentiment analysis, entity extraction, query routing, unstructured to structured text processing, creative writing, and compliance checks.

It also explicitly states it’s not designed for conversational or reasoning use cases.

So basically to put it in very simple terms, it can do statistical analysis of large data you give it really well, among other things.

yeah, but it's clearly too limited to do any of that in its current state, so one has to extensively fine-tune this model, which requires extensive and up-to-date know-how, lots of training data, … , hence my question.