Most prompt advice fails the moment you’re tired, rushed, or trying to reuse it a week later. The examples look impressive in a demo and quietly implode in real work. Not because you’re bad at prompting, but because most prompts are built like fragile art projects instead of boring tools.
This post lays out a minimal prompt structure that survives repetition. It is not clever. It is not optimized for social media screenshots. It works across tasks because it separates thinking from decoration.
Why “clever prompts” fail under pressure
Clever prompts usually mix too many intentions into one blob. They rely on vibes, implied context, and a lot of hope. When they work, it’s often because you were mentally filling in gaps the model didn’t actually have.
Under pressure, three things go wrong. First, you skip context because you assume it’s obvious. Second, you change wording slightly and get a wildly different result. Third, you forget what mattered in the first place and start stacking instructions until the output collapses under its own weight.
A stable prompt does the opposite. It makes context explicit, separates concerns, and stays readable when you come back to it later with half a brain and a coffee that’s already gone cold.
The five parts of a stable prompt
A prompt that holds up over time usually has the same core pieces, regardless of task. Not as a checklist you obsess over, but as a structure you recognize.
First, define the role. This tells the model how to frame decisions, not what personality to cosplay. “You are a support agent responding to customer questions” beats “act like a friendly expert ninja.”
Second, define the job. This is the actual task, stated plainly. What you want produced, not how impressive it should sound.
Third, provide the inputs. This is the raw material. Source text, bullet points, notes, transcripts. If it matters, put it here instead of assuming the model remembers it.
Fourth, specify the output format. Paragraphs, bullets, table, short answer, long answer. This alone fixes more bad outputs than most prompt tricks.
Finally, add constraints. Tone, length, things to avoid, rules that matter. Constraints come last because they shape the output, not the understanding of the task.
If you keep these parts separate, the prompt stays legible. If you blur them together, debugging becomes guesswork.
How to add constraints without making it rigid
Constraints are where people usually panic and overcorrect. They either add none and hate the result, or they add so many rules that the model can’t move.
The trick is to constrain outcomes, not process. Say what must be true about the output, not how the model should think its way there. For example, “Keep it under 200 words and avoid marketing language” is useful. “Think step by step and sound human but professional and friendly but authoritative” is noise.
If a constraint keeps breaking outputs, it probably belongs earlier in the structure as part of the job or the output format. If it still breaks things, it might not be a real requirement. That happens more often than people like to admit.
One filled example, with commentary
Here’s a complete prompt using the structure, with commentary inline so you can see what each part is doing.
Role
You are a customer support lead writing clear, neutral responses.
This sets decision boundaries. No personality theater, just framing.
Job
Draft a reply to a customer question that resolves the issue and explains the next step.
Plain language. No adjectives doing emotional labor.
Inputs
Customer question:
“Why was my invoice higher this month than last month?”
Billing notes:
– Usage increased by 18 percent
– One-time setup fee applied
– No pricing change
Everything the model needs is visible. No guessing.
Output format
Two short paragraphs.
First explains the reason.
Second explains what happens next.
This prevents rambling and forces structure.
Constraints
Avoid apologetic language.
Do not mention internal systems.
Keep under 120 words.
These shape tone and scope without strangling the output.
You can reuse this prompt for FAQs, internal replies, or summaries by swapping only the inputs and the output format. The structure stays intact, which is the whole point.
How to adapt it across use cases
This structure scales because it isolates change. If you’re summarizing a meeting, the role becomes “project coordinator,” the inputs become notes or a transcript, and the output format might be a bulleted summary with action items. The rest stays boring and reliable.
A support lead can use the same skeleton for customer FAQs, internal escalation notes, and leadership summaries. Only the inputs and output format change. That consistency is what makes it usable across a team instead of living in one person’s head.
If you find yourself rewriting the whole prompt every time, the structure is wrong. If you can reuse it without thinking, you’re doing it right.
The goal here isn’t to sound smart. It’s to get predictable results when you don’t have time to babysit the model. Boring prompts age better.