You need to be smarter (or rather: more knowledgeable in the problem domain) than the model to be able to use it efficiently. Hallucinations are still a problem occasionally but a bigger one is failure of imagination. Even Claude Fable lacks a holistic understanding of many domains it wasn't obviously trained on. The biggest problem with AI (if we assert that LLMs can be the basis of AI) is that these models will make mistakes that exist in an entirely different category of the kind of mistakes humans will make.
As an autistic this is painfully obvious to me but: much of human interactions operates on rules that are not only unspoken but often unacknowledged or even outright denied - not just that, but most rules are also highly contextual and rarely treated literally. E.g. corporate guidelines mostly don't exist to be followed (and following them will often result in punishment) but to be able to shift blame - but you need to know for which ones this is the case and for which ones it isn't. This is further complicated because any AI or AI vendor openly making such distinctions would be rejected - AI would not only need to understand all this nuance but also this additional meta layer.
A simple example would be "work to rule": in many professions work processes are heavily regulated (whether by law or by corporate guidelines) but the unspoken assumption is that you know which rules you should ignore and which ones you actually need to follow - but if you tried to find this out by asking "is this a rule I need to follow or not" you would get the clearly incorrect answer that all rules must be followed; of course if you did follow all the rules (aka "work to rule") you would be disciplined for failing to meet quotas (because you can't be disciplined for following the rules).
As an autistic this is painfully obvious to me but: much of human interactions operates on rules that are not only unspoken but often unacknowledged or even outright denied - not just that, but most rules are also highly contextual and rarely treated literally. E.g. corporate guidelines mostly don't exist to be followed (and following them will often result in punishment) but to be able to shift blame - but you need to know for which ones this is the case and for which ones it isn't. This is further complicated because any AI or AI vendor openly making such distinctions would be rejected - AI would not only need to understand all this nuance but also this additional meta layer.