How do you tell if a prompt is actually good?
I look at prompts all day. Not because I'm some kind of prompt engineer. But because using AI well is how I get my work done faster than I ever have before.
After enough reps, you start to notice something. When a prompt doesn't work, most people just rewrite it. Change some words, add more detail, & try again. Sometimes the 3rd version works. But you can't tell what actually fixed it, so you can't repeat it next time.
I got tired of guessing. So I started paying attention to what kept going wrong. After a while, the same 5 things kept showing up. Not a checklist I run before every prompt. More like a mental shortcut for when something's off and I can't tell why.
1. Can you state the task in 1 sentence?
If you can't say what the prompt is asking the model to do in 1 sentence, the model can't figure it out either. Long prompts aren't the problem. Buried asks are.
To clarify, a prompt can have 3 or 10 asks. That's fine.
What matters is that you can explain each one simply. If you can't state it, the model can't follow it, and you won't even notice when the output misses it.
2. Does the framing actually change the output?
"Act as a world-class marketing strategist" sounds like it should matter. Paste the prompt with and without that line. If the output doesn't change, the framing is decoration.
I still use roles though. When I write "act as a financial advisor," I'm not expecting the model to suddenly have a CFP license. I'm putting myself in a headspace where I ask better questions. The role shifts my thinking, not the model's.
Just know which one you're doing.
3. Did you specify what the answer should look like?
Format, length, structure, & sections. If you leave the output shape wide open, the model picks for you. Sometimes that's fine.
Usually it's not.
4. Does the prompt handle failure before it happens?
I'll be honest. I don't write failure instructions on the first try most of the time. I don't know what bad output looks like until I see it. The model does something wrong, & then I say "don't do that." Like correcting a kid. You don't know what they're going to do until they do it.
So this question is less "did you build in guardrails" & more "the prompt keeps giving you bad output, did you think to tell it what to stop doing?"
5. Will you get a real answer or generic advice?
Ask the model, "how do I get better at my job" & you get 10 bullet points that apply to everyone and help no one. A good prompt forces a specific answer that the model wouldn't give unprompted.
The exception is when you want generic. Sometimes I want the model to just throw ideas at the wall. Not accurate, not tailored, just a pile of options I can react to.
That's brainstorming, not a prompting failure. The question is whether you got generic output on purpose or by accident.
I'm still learning. If you've got something that works for you that I didn't cover, I'd rather hear it than assume I've figured this out.