Everyday use is not (usually) one of those contexts. Prompting an LLM works much better with an anthropomorphized view of the model. It's a useful abstraction, a shortcut that enables a human to reason practically about how to get what they want from the machine.
It's not a perfect metaphor -- as one example, shame isn't much of a factor for LLMs, so shaming them into producing the right answer seems unlikely to be productive (I say "seems" because it's never been my go-to, I haven't actually tried it).
As one example, that person a few years back who told the LLM that an actual person would die if the LLM didn't produce valid JSON -- that's not something a person reasoning about gradient descent would naturally think of.