A guy on Reddit spent 45 minutes rewriting the same prompt last week. Third version worked. He posted it as a win. But he had no idea what actually fixed it, which means he can’t repeat it next time.
That’s not a prompting skill. That’s a coin flip.
If you use AI to get real work done, random wins don’t compound. You need to know what made the prompt work so you can build the right version first, every time.
🔍 Why This Is Worth Five Minutes of Your Life
Bad prompts don’t look broken. They just return mediocre output and you assume you need to try again. So you rewrite with slightly different words, same buried ask, same missing structure. Third version kind of works and you move on.
The problem is you never figured out why. So next week you’re back at square one, rewriting the same prompt from scratch.
There’s a faster way. Not a checklist you run before every prompt. More like five questions that catch the most common failures before you waste another hour guessing.
The difference between someone who gets consistent results from AI and someone who gets lucky every third try usually comes down to one thing: they can explain what worked. That explanation is the skill. The output is just evidence.
📋 The Five-Question Diagnostic
1. Can you state the task in one sentence?
Before writing anything, finish this sentence: “I want the model to ___” If you can’t complete it cleanly, you don’t know what you’re asking yet. Long prompts are fine. Buried asks are the problem. The model can’t find your actual request if you buried it in three paragraphs of context.
Here’s the quick test: “Write me content about marketing” is not a task. It’s a category. “Write a 200-word Instagram caption for a solopreneur selling a $97 freelancing course, in a confident and direct tone” is a task. The more specific the blank you fill in, the less guessing the model has to do on your behalf.
2. Does your framing actually change the output?
Test it. Paste your prompt with and without “act as a world-class expert in X.” If the output is identical, the framing is decoration. You can keep it if it helps you think more clearly. Just don’t expect it to do heavy lifting on the model’s end.
Where framing does help is in constraining the angle. “Write this as someone explaining it to a skeptical CFO” gives the model a clear voice and a clear audience. That tends to produce tighter output than “write this professionally.” Specificity beats status every time.
3. Did you specify what the output should look like?
Format, length, structure, sections. If you leave the shape wide open, the model picks for you. Sometimes it guesses right. Usually you get something technically correct and completely unusable in the context you actually need it.
Instead of hoping the model knows you need three bullet points and a subject line, just say it. “Give me five bullet points, each under 20 words, followed by one subject line option.” Two sentences of formatting instruction can save you an entire rewrite cycle. That’s a good trade.
4. Did you tell it what to stop doing?
Not for first drafts. This is for when you’ve already seen bad output and still haven’t said “don’t do that.” The model will keep doing it. You don’t need to predict every failure upfront. You just need to stop ignoring the ones you’ve already seen.
If it keeps adding a disclaimer you don’t want, or padding the intro with three sentences of throat-clearing before getting to the point, say so explicitly. “Skip the intro. Start with the first actionable step.” Negative constraints are just as powerful as positive instructions, and most people forget to use them.
5. Did you get a specific answer or generic advice?
Ask “how do I get better at my job” and you get ten bullet points that apply to everyone and help no one. A good prompt forces a specific answer the model couldn’t hand to just anyone. If you got generic output, ask yourself: was that the goal, or did it happen by accident?
One easy fix: add your actual context. “I’m a freelance copywriter, three years in, mostly B2B SaaS clients, struggling to raise my rates” produces something you can actually use. The model needs the walls to give you something that fits inside them.
💡 One Distinction Worth Making
Brainstorming prompts and task prompts are different animals. If you asked for a pile of rough ideas to react to, generic output is exactly what you wanted. Don’t call that a failure.
Also, roles in prompts often shift your thinking more than the model’s behavior. “Pretend you’re a CFO reviewing this pitch” makes you ask sharper questions. That’s genuinely useful. Just know which one you’re doing.
The broader point: the goal of this diagnostic is not to add more words to your prompt. It’s to find the one thing that’s actually causing the problem. Fix that. Nothing else. Prompts that grow by 300 words every rewrite cycle usually have the same buried issue the whole time, just dressed in more elaborate clothing.
🚀 Try It Right Now
Pull up the last prompt that gave you bad output. Run it through all five questions. Find the first one that breaks. Fix only that.
You’ll know exactly what changed. And next time you’ll build the right version first instead of landing on it by accident.
How do you tell if a prompt is actually good?
by u/promptTearDown in ChatGPTPromptGenius