I have had in person conversations with multiple people who swear they have fixed the AI hallucination problem the same way. “I always include the words ‘make sure all of the response is correct and factual without hallucinating’”
These people think they are geniuses thanks to just telling the AI not to mess up.
Thanks to being in person with a rather significant running context, I know they are being dead serious, and no one will dissuade them from thinking their “one weird trick” works.
All the funnier when, inevitably, they get screwed up response one day and feel all betrayed because they explicitly told it not to screw up…
But yes, people take “prompt engineering” very seriously. I have seen people proudly display their massively verbose prompt that often looked like way more work than to just do the things themselves without LLM. They really think it’s a very sophisticated and hard to acquire skill…
“Do not hallucinate”, lol… The best way to get a model to not hallucinate is to include the factual data in the prompt. But for that, you have to know the data in question…
It’s incredible by now how many LLM users don’t know that it merely predicts the next most probable words. It doesn’t know anything. It doesn’t know that it’s hallucinating, or even what it is saying at all.
One things that is enlightening is why the seahorse LLM confusion happens.
The model has one thing to predict, can it produce a spexified emoji, yes or no? Well some reddit thread swore there was a seahorse emoji (along others) so it decided “yes”, and then easily predicted the next words to be “here it is:” At that point and not an instant before, it actually tries to generate the indicated emoji, and here, and only here it falls to find something of sufficient confidence, but the preceding words demand an emoji so it generates the wrong emoji. Then knowing the previous token wasn’t a match, it generates a sequence of words to try again and again…
It has no idea what it is building to, it is building results the very next token at a time. Which is wild how well that works, but lands frequently in territory where previously generated tokens back itself into a corner and the best fit for subsequent tokens is garbage.
I didn’t think prompt engineering was a skill until I read some of the absolute garbage some of my ostensibly degree-qualified colleagues were writing.
I have had in person conversations with multiple people who swear they have fixed the AI hallucination problem the same way. “I always include the words ‘make sure all of the response is correct and factual without hallucinating’”
These people think they are geniuses thanks to just telling the AI not to mess up.
Thanks to being in person with a rather significant running context, I know they are being dead serious, and no one will dissuade them from thinking their “one weird trick” works.
All the funnier when, inevitably, they get screwed up response one day and feel all betrayed because they explicitly told it not to screw up…
But yes, people take “prompt engineering” very seriously. I have seen people proudly display their massively verbose prompt that often looked like way more work than to just do the things themselves without LLM. They really think it’s a very sophisticated and hard to acquire skill…
“Do not hallucinate”, lol… The best way to get a model to not hallucinate is to include the factual data in the prompt. But for that, you have to know the data in question…
“ChatGPT, please do not lie to me.”
“I’m sorry Dave, I’m afraid I can’t do that.”
That’s incorrect because in order to lie, one must know that they’re not saying the truth.
LLMs don’t lie, they bullshit.
It’s incredible by now how many LLM users don’t know that it merely predicts the next most probable words. It doesn’t know anything. It doesn’t know that it’s hallucinating, or even what it is saying at all.
One things that is enlightening is why the seahorse LLM confusion happens.
The model has one thing to predict, can it produce a spexified emoji, yes or no? Well some reddit thread swore there was a seahorse emoji (along others) so it decided “yes”, and then easily predicted the next words to be “here it is:” At that point and not an instant before, it actually tries to generate the indicated emoji, and here, and only here it falls to find something of sufficient confidence, but the preceding words demand an emoji so it generates the wrong emoji. Then knowing the previous token wasn’t a match, it generates a sequence of words to try again and again…
It has no idea what it is building to, it is building results the very next token at a time. Which is wild how well that works, but lands frequently in territory where previously generated tokens back itself into a corner and the best fit for subsequent tokens is garbage.
Fabulating even!
I didn’t think prompt engineering was a skill until I read some of the absolute garbage some of my ostensibly degree-qualified colleagues were writing.
Have you tried to not be depressed?