A Reddit post in r/PromptEngineering lays out a simple 5-step framework that I think is worth stealing. It isn’t magic. It just stops you from sending the model a vague one-liner and hoping for the best. Most of us write prompts the way we text a friend, assuming the other side already knows the backstory. The model doesn’t, and this framework forces you to fill in that backstory before you hit enter.
The breakdown
- Role. Tell the model what kind of specialist to be, and ask it to prioritize accuracy and practical advice. This sets the tone. It’s the lightest part of the framework, so don’t overthink it. “Act as a financial analyst who values accuracy over speed” is plenty. You don’t need a three-paragraph character backstory.
- Objective. One sentence on exactly what you want done. “Create a 14-day plan” beats “help me with marketing.” A good test: if a coworker read only that sentence, could they tell when the job is finished? If not, tighten it until they could.
- Context. Audience, main goal, available resources, and constraints. This is where most weak prompts fall apart, because the model is guessing about your situation. Think budget, time per day, tools you already pay for, and what you’ve tried before. Two minutes of typing here saves ten minutes of rewriting later.
- Output requirements. Say how you want the answer shaped. Lead with the best answer, give actionable steps, include examples, explain trade-offs, and separate verified facts from assumptions. Ban filler while you’re at it. If you want a table, a checklist, or a word limit, this is the place to say so.
- Quality check. Ask the model to check every requirement, flag missing info, fix inconsistencies, and ask up to three focused questions if something essential is missing instead of guessing. That last part is underrated. A model that asks you a question is a model that isn’t making something up.
The before and after
Weak: “Create a marketing plan for my small business.”
Better: “Act as a digital marketing strategist for a small business with a $100 monthly budget. Create a 14-day organic plan targeting entrepreneurs interested in AI productivity tools. Include daily activities, platform recommendations, content ideas and measurable KPIs. Prioritize free tools and separate realistic expectations from guaranteed outcomes.”
Same request. The second one is a concrete problem the model can actually solve. Notice what changed: a number (the budget), a timeframe, a named audience, and a ban on empty promises. None of those are fancy. They’re just facts you already knew and forgot to share.
Where the community pushed back
The comments are where it gets interesting, and they’re mostly right.
- 🔍 Step 5 has a catch. A model checking its own answer in the same chat often just agrees with itself. Several people hand the output to a different model in a fresh chat instead. The original poster admits this too: self-checking doesn’t guarantee accuracy.
- 🧩 Examples beat rules. One commenter said a short sample of the output you want fixes more than any style instruction. Describe a style and the model drifts. Show it and it mostly stays put.
- 🎯 Structure can’t save weak input. Someone ran the framework on a vague story request and still got a generic fantasy plot. If you don’t know what you want, the template won’t know either.
Another reader added that order matters: lead with the objective and outcome, then constraints, and let the role come last. That’s worth testing on your own tasks. Run the same prompt both ways and compare the results side by side. It takes five minutes and you’ll learn how your favorite model actually behaves.
Use cases
- Content planning, where audience and constraints change the whole output
- Research summaries, where you need facts split from guesses
- Business plans on a tight budget
- Any recurring task you want to turn into a reusable template, like weekly reports, client emails or meeting recaps
Prompt of the Day
“Act as a [specialist] who prioritizes accuracy and practical advice. Your task is to [exact outcome]. Context: audience is [audience], goal is [goal], resources are [tools/budget], constraints are [limits]. Start with the most useful answer, give actionable steps and specific examples, explain trade-offs, and label what is verified versus assumed. Before finalizing, check that every requirement is covered. If essential information is missing, ask up to three focused questions instead of guessing.”
Paste in a sample of the output you want at the end. That one addition fixes more than most rewrites. Save the filled-in version somewhere you can find it, because the second time you use it takes about thirty seconds.
Your move
Take one prompt you use every week and rebuild it with these five parts. Add a sample output. For anything that matters, run the check in a different model, not the same chat. Then tell me which part changed your results the most: context, constraints, examples or the evaluation step.
Frequently Asked Questions
Q: Should I keep Step 5, the quality check?
Users disagree here. Some say asking the model to check its own answer in the same chat mostly gets agreement and nothing useful to fix, so they paste the answer into a fresh chat with a model from a different vendor and ask it to find problems. Others say the check works, but only with reasoning models, so a non-reasoning model will just rubber-stamp its own output.
Q: Does the order of the sections matter?
One commenter says yes. They lead with the objective, then the outcome they want, then the constraints, and put the role last unless accuracy or creative direction is the main concern. Their reasoning is that the role mostly shapes the final wording, while the objective and constraints drive what the model actually works on.
Q: What if my starting request is vague?
The framework can’t rescue weak input. One user tried it on a vague ‘write a story’ request and still got a generic fantasy plot. Before you fill in the template, decide on the one specific thing you want, such as the genre, the length, and who will read it. Then the structure has something real to work with.
Q: Can one example replace a long list of style rules?
Often, yes. A commenter found that describing a style makes the model drift over a longer conversation, while a short sample of the output you want keeps it on track. Add a brief sample of what good looks like to the Output Requirements section, and keep it short so the model picks up the tone without copying every word.
Stop Asking AI for Better Answers. Try This 5-Step Prompt Framework Instead.
by u/CashandResources in PromptEngineering