alt Hacker NewsAs part of a previous job I needed to audit internal AI usage from a largely non-technical employee population.
The prompts were, predictably, really bad. Broken English, sentence fragments, vague requests, lack of context. Yet somehow, the users always got the answer they were looking for. It might have taken a few extra turns with questions from the model, but the end result was the same.
It's humbling, but a flowery, carefully crafted prompt is at best slightly more efficient than a "CAN A DOG BE EATIN SUN FLOWER SEED?" peasant prompt.
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"dog sunflower seeds" is all the context an LLM needs, any other words are extraneous.
They don't need to be told that you're asking about the safety of eating them, because they can infer that based on the fact that a very large percentage of any text linking dogs to sunflower seeds is obviously going to be about the safety of the dog eating them.
Even pre-LLM that would have been a perfectly sufficient google search for the same information.
> Broken English, sentence fragments,
Why is would that be "bad prompt"? It is machine inpit, if machine can interpret fragment all the better.
You call that a peasant prompt, but it's actually almost perfect. Couple notes, but it's 95% of the way there. "Can a dog eat sunflower seed" is probably the perfect version, just 1 extraneous word in this version.
Unless the user wanted to know if a cat could eat sunflower seed or something.