This is 98% the right answer, but you drop them somewhere that keeps them intact, and believable enough so that people take them, and spend the rest of the weekend going to thrift stores trying to find an external floppy drive, and the next month trying to figure out how to get their iPhone to mount it.
Your experience highlights what current iterations of LLMs are not well suited for, so I understand if that’s what you were hoping to achieve, why you were left wanting, or disillusioned.
There’s a lot of things that LLMs are really good at, or incredibly useful for, such as ingesting large bodies of text, and then analyzing them based on your ability to create well thought out prompts.
This can save you hours and hours, of reading time, and it’s something that you can verify the answer on relatively quickly, to double check the LLMs response accuracy.
They’re also good at doing something Google used to be good at, but sucks at now. Which enabling you to describe process, simple or complicated, short or long, that you either can’t recall the name of, or aren’t even sure where it’s called, and letting you know exactly what it is. Also, easily verifiable.
There’s plenty of other things too, but just remember that they are tools, not magic, or sentient intelligence.
The models are not real time, but there are tricks to figure out it’s most recent dates of ingestion, such as asking topical entertainment or news questions, but don’t go looking for a real-time information.
Also, I have yet to find a model that can provide an actual URL and specific source for anything it generates, which is why it’s a good practice to use them to do tasks, or get information, that would take you longer to do, or get, manually, but that can be easily verified once you receive it.