AI Gives Lists When Execution Is Needed
You asked the model to do the work; it returned an outline of how someone could do the work.
Articles tagged with #Prompt quality
You asked the model to do the work; it returned an outline of how someone could do the work.
You listed five rules. The model honored four and quietly dropped the one that actually mattered.
You asked for a small polish; you got a complete rewrite that lost your voice and structure.
You gave criteria, but the criteria are themselves vague — "engaging", "professional", "innovative" — so the model interprets freely.
Two parts of your prompt fight each other, so the model averages them and produces something nobody asked for.
A casual aside at the end of your prompt overrides the careful rules you wrote at the top.
Your prompt has three paragraphs of context and one buried sentence asking for the deliverable — the model summarizes the background instead.
You described what you want in words; the model approximates. Add one concrete example and the approximation becomes a match.
"Professional but friendly, formal but warm, expert but accessible" gives the model conflicting tone targets.
You gave a partial spec; the model invented the rest to look complete.
"Do not be generic" tells the model what not to do without telling it what to do.
You wanted a structured answer; you got 600 words of paragraphs you cannot copy-paste anywhere.
Without a success criterion, "good" is whatever the model thinks looks confident.
The answer reads beautifully and yet you cannot use any of it without rewriting.
Without a decision rule, "best" defaults to whatever the model thinks sounds confident.
A prompt that worked great elsewhere produces nonsense for your current task because the assumptions do not transfer.
"Please make it amazing!" feels persuasive but tells the model nothing it can act on.
You pasted everything as a flat block. The model cannot tell which lines are critical and which are background.
You attached three documents and the model treated them as equally authoritative — including the outdated draft.
Without an audience, the model writes for an imaginary average reader and pleases no one.
You asked a sweeping question and got a sweeping non-answer.
"You are a senior engineer" sets a vibe but does not change the deliverable. You still need rules, format, and examples.
"Be warm and conversational, return strict JSON" is two requests pulling in opposite directions.
5+ examples can crowd out the instruction or push the model to imitate examples instead of executing the task.
You stacked five tasks in one prompt; the model did one well, one badly, and partially answered three.
You asked the model to do one thing; it also did three adjacent things you did not want.
You asked a real question and got a "depends on your situation" non-answer.
The model confidently produced wrong facts, citations, or API calls.
Tone, voice, or format keeps changing turn over turn even though the prompt is the same.
You asked for an edit to one part — and AI quietly rewrote logic elsewhere.
The prompt is detailed and exhaustive, yet the answer is vague, off-target, or generic.
A legitimate task was refused or partially blocked by the safety system.