Your point is valid if you believe LLM/Generative AI is deterministic; it is not. It is inference-based, and thus it provides different answers even given the same input at times.
The question then becomes, "How wrong can it be and still be useful?" This depends on the use case. It is much harder for applications that require high deterministic output but less important for those that do not. So yes, it does provide wrong outputs, but it depends on what the output is and the tolerance for variation. In the context of Question and Answer, where there is only one right answer, it may seem wrong, but it could also provide the right answer in three different ways. Therefore, understanding your tolerance for variation is most important, in my humble opinion.
The question then becomes, "How wrong can it be and still be useful?" This depends on the use case. It is much harder for applications that require high deterministic output but less important for those that do not. So yes, it does provide wrong outputs, but it depends on what the output is and the tolerance for variation. In the context of Question and Answer, where there is only one right answer, it may seem wrong, but it could also provide the right answer in three different ways. Therefore, understanding your tolerance for variation is most important, in my humble opinion.