Seems easy. Have a set of vague requests and train it to ask for clarification instead of guessing.
As I said, it's possible to train it to ask for clarification, but it's not clear how to reinforce that response in a way that correctly maps on to the absence of data rather than arbitrary embedding proximity. You can't explicitly train on every possible scenario where the AI should recognize its lack of knowledge.
If the solution were easy or obvious the problem would likely have already been solved no?
We've only had ChatGPT and the like for a few years. It took Ford longer to make automatic transmissions.
So it is hard? Not easy? I would agree with that position. I think the analogy with automatic transmissions misses though. Programming actual intelligence into a computer seems orders of magnitude more complex and difficult than building the gearbox for a car.
I'm saying it shouldn't be that hard, but it's just one of a long list of features that the people whose job it is to do are working on.
It is hard in the sense that it's an unsolved problem that emerges due to the way LLMs work. Perhaps some clever ML PhD will come up with a technique to solve it, but right now there's no clear solution.
How does it identify what's vague?
Many ways. 1) Hire some humans to label the data. 2) Let the user give you feedback. 3) Ask another LLM.