LLMs works by converting your question into a list of numbers and projecting that list, like a shadow, into a high-dimensional space which was constructed through training on other lists of numbers. Where the projection lands gives a new list of numbers, which are then translated back into words.

Because of the way the model (i.e. the projection surface) was constructed, the strings returned look plausible. However, you're still just seeing the number-back-to-language translation of a vector which was guessed by statistical inference.