The tension is that most users want something differently generic when they provide a short or vague prompt for creative work. They want to treat the AI like an ideal creative consultant, who not only knows the ideas that are played out and tired but also the ones that they themselves have delivered a dozen times already. They don't want to specify deeper constraints or survey all the cliches and tropes, but they also don't want to generate something that they -- or the people they share the output with -- will immediately recognize as cliche themselves.

But LLM's are structured to do exactly the opposite. They discover specific commonalities and tropes and most-frequently-sufficient wells or ley lines that they gravitate towards from whatever their starting point it. You can nudge them away from the most generically cliche tropes by being more detailed in your prompt, but they'll still just find the nearest most right cliche from there.

It's what they do and the same quality that makes them tiring and predictable for creative output makes them very useful for things like business communication or coding where landing on such cliches is ideal.

To get more variety out, it can be useful to use Perchance-like text-randomization techniques to shuffle combinatoric variations on a prompt before sending it to the model. But that technique is best made available through some kind of special purpose harness for the creative task, not basic chat or direct curl calls to the API.