Another example from my own tinkering is something I've wanted for a while which was not really achievable with previous NLP machine learning or heuristic systems, which is pulling out key information from a conversation and formatting it into a machine readable format (granted formatting it isn't great unless you use other techniques but those do exist).
For my case, I want to generate some dialogue from a character using a certain LLM that's good at that, have another LLM look at the generated dialogue of the NPC and tell the game certain things:
Was a quest/task offered?
Were enemies mentioned? And if so, what types? From those types and the types of enemies actually in my game, which ones should I spawn?
What locations were mentioned in relation to the task?
All of these things were technically possible not using LLMs with very cumbersome and hard to utilize NLP libraries that can manually break down written English text into Subject-Object-Verb or other similar structured data, but getting at the actually interesting data was much more difficult and brittle - if your code that looked at the returned extracted language modeling didn't account for something, or if the text was poorly written grammatically etc, it could easily (and usually did) fail, whereas all of the above are trivial for an LLM to complete mostly-correctly.
Another example from my own tinkering is something I've wanted for a while which was not really achievable with previous NLP machine learning or heuristic systems, which is pulling out key information from a conversation and formatting it into a machine readable format (granted formatting it isn't great unless you use other techniques but those do exist).
For my case, I want to generate some dialogue from a character using a certain LLM that's good at that, have another LLM look at the generated dialogue of the NPC and tell the game certain things:
All of these things were technically possible not using LLMs with very cumbersome and hard to utilize NLP libraries that can manually break down written English text into Subject-Object-Verb or other similar structured data, but getting at the actually interesting data was much more difficult and brittle - if your code that looked at the returned extracted language modeling didn't account for something, or if the text was poorly written grammatically etc, it could easily (and usually did) fail, whereas all of the above are trivial for an LLM to complete mostly-correctly.