LLMs: unlimited use cases, all with different performances per model and approach, where a high performance on use case A doesn't mean high performance on use c

LLMs: unlimited use cases, all with different performances per model and approach, where a high performance on use case A doesn't mean high performance on use case B. And high performance using approach X for use case A doesn't mean high performance using approach Y for that same use case. The use case one is bigger than the approach one, but both play a role. Most people only use LLMs for a very specific set of tasks using the same approach every time, so they base their view of them on solely the performance on this task. That explains all of it.