LLMs are not created to chat, they’re literally what the name says - language models. They are very complex statistical models of the joint causal probability of all possible words given the previous words in the context window. There’s a common misconception that they’re “made for chat” by the wider public because ChatGPT was the first “killer application”, but they are much more general than that. What’s so profound about LLMs to AI and NLP engineers is that they’re general purpose. That is, given the right framework they can be used to complete any task expressible in natural language. It’s hard to convey to people just how powerful that is, and I haven’t seen software engineers really figure this out yet either. As an example I keep going back to, I made a library to create “semantic functions” in Python which look like this:

@semantic
def list_people(text) -> list[str]:
    '''List the people mentioned in the given text.'''

That is the entire function, expressed in the docstring. 10 months ago, this would’ve been literally impossible. I could approximate it with thousands of lines of code using SpaCy and other NLP libraries to do NER, maybe a dictionary of known names with fuzzy matching, some heuristics to rule out city names or more advanced sentence structure parsing for false positives, but the result would be guaranteed to be worse for significantly more effort. Here, I just tell the AI to do it and it… does. Just like that. But you can’t hype up an algorithm that does boring stuff like NLP, so people focus on the danger of AI (which is real, but laymen and news focus on the wrong things), how it’s going to take everyone’s jobs (it will, but that’s a problem with our system which equates having a job to being allowed to live), how it’s super-intelligent, etc. It’s all the business logic and doing things that are hard to program but easy to describe that will really show off its power.