You correct your AI once. Then again, then a third time, and the landing page copy still misses what you actually wanted. Most people respond by rewriting the entire prompt from scratch, which almost never fixes the real problem.
A breakdown posted on r/PromptEngineering caught the exact moment prompts fall apart, and it lands somewhere you would not expect. The original poster strips a solid-looking copywriter prompt down to its layers: role, constraints, clarification, and terminology, all present and accounted for. One layer is missing on purpose: measurable criteria, the actual numbers for quantity, length, and tone that turn taste into something a model can hit.
Here is why I find this worth your time: the failure is not sloppy writing, it is vagueness the model cannot detect on its own. A model only asks about what it reads as unclear, and a word like 'several' never trips that wire. Fix this one layer and the correction spiral stops for good.
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The prompt that looks finished but isn't
The example reads airtight on paper. You are a copywriter, write several persuasive versions of landing page copy with a call to action, refuse anything outside the role, and ask if anything is unclear.
Role, there. Constraints, there. Clarification, there.
But nowhere does it say how many versions, how long each one runs, or what 'persuasive' means in this context. That gap is the whole story.
Five corrections, five guesses
Watch it break in slow motion. The user says write the versions, and the model assumes three, because training data says 'several' usually means three. The user says more, so it jumps to ten.
Too much, so it cuts back. The copy is weak, so it adds exclamation marks. Now it is pushy, so it strips them back out.
Five rounds, five fresh guesses, each one stacked on the last.
What the user wanted the whole time: five versions, 100 words each, calm tone, zero exclamation marks. He never said it out loud. He assumed 'several' and 'persuasive' already carried the meaning.
Here is the part that stings for me: 'several' and 'persuasive' don't register to the model as unclear. They register as vague. And vague is a different trigger than unclear, so the model never stops to ask.
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The fix is a block, not a rewrite
The author's answer is not 'be more specific' bolted onto the end of your prompt. It is a dedicated criteria block that forces the model to confirm numbers before it generates anything. Quantity as a number, length as a number, tone as a named style, calls to action as a number or zero.
The mechanism I like most: the vagueness check. Before requesting a criterion, the model asks three questions. Can this be read two ways?
Does it depend on taste? Does it have a numerical form? If it fails, treat the criterion as undefined and ask, never substitute your own value.
That flips the model from passive to active. It hunts for the gap instead of waiting for you to notice it. And there is a side effect the creator flags that lands harder than the fix itself: the block often exposes that you never knew the number either.
'Several' becomes an actual conversation about what you meant, maybe for the first time.
The practical move is small. Take a working prompt, list every adjective that isn't a number, and run each one through the check. Drop the block in right after your role and constraints, then answer the model's first questions with real numbers, not more adjectives.
Reuse it across copywriting, code review, content briefs.
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Where vague ends and subjective begins
One commenter pushed back, and the objection is fair. This can conflate ambiguity with genuinely subjective direction like 'make it feel more premium.' Some criteria will never reduce to a clean number.
So the nuance matters: measurable is not the same as numeric. '3 versions, 150 to 200 words, conversational tone' is measurable, and it still leaves room for feel. For subjective feedback, give reference points instead of adjectives, and use comparative language across rounds so the model refines rather than resets.
One block, four numbers, before you write
Open your last messy prompt and paste in a short criteria block that names exact numbers for quantity, length, tone, and CTAs, plus a line telling the model to flag anything vague before it writes.
See how the criteria block and vagueness check fit together in the full breakdown.
Worth 10 minutes if your prompts keep spiraling from three to ten to 'too much' and you want to know exactly which layer broke.
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Poll: The model reads "several" and quietly picks a number. Whose fault is that?
