Large Language Models have no concept of truth or falsehood, they just generate a stream of tokens that are statistically similar to the body of text their neural nets were generated from. They have no memory or modelling or reasoning capability, they just generate text that humans are likely to see as a plausible continuation of the text they were prompted with. Plausible, not factual, or safe, secure, or sane, just something that might have been in the source text if the prompt had also been in there.
One way to apparently increase the chance that the results will seem appropriate is to test the prompt against that body of text. If the prompt is something that matches it well, then it's less likely to produce surprising results. This is not a guarantee, by any means, but it does help. If it doesn't, then the software could indicate that and decline to generate anything.