I’ll admit something. For a long time I typed lazy questions into ChatGPT and expected brilliance back. “Tell me about marketing.” That kind of thing. The answers were flat, generic, forgettable. I blamed the tool. Then I came across a post from an AI professional that flipped my whole thinking, and honestly, I wish I’d read it a year earlier.
The original poster made the same mistake when they first got serious about AI. They’d ask something vague and wait for magic. What came back was filler they could’ve Googled in 30 seconds. So they changed one habit, and everything shifted.
The fix? Stop treating prompts like search queries. Start treating them like briefs.
The tool isn’t the problem. The prompt is.
Here’s the mental model the creator shared, and it stuck with me right away. Think about how you’d brief a new hire before handing off a project. You’d give them the goal, the background, the constraints, and a clear picture of what “done” looks like. You wouldn’t just say “do marketing” and walk away. The expert applies that exact logic to AI, and the jump in output quality was immediate.
Then they broke down what a genuinely great prompt is made of. Turns out it’s not one magic sentence. It’s 10 components, each pulling its own weight. Here’s the full breakdown, with the percentages the author assigned to show how much each piece matters.
The 10 building blocks of a perfect prompt
- Objective (15%): Define the task with zero fog. “Tell me about marketing” gets noise. “Create a 90-day marketing strategy for a SaaS startup targeting small businesses” gets a plan you can actually use. Specificity is the whole game here.
- Role (10%): Tell the model who to be. Senior software engineer. Expert copywriter. Startup advisor. As the creator points out, each role activates a totally different reasoning pattern, so the same question gets answered from a sharper angle.
- Context (20%): This one carries the most weight, and it makes sense. Hand over your business type, industry, audience, goals, and constraints. More context almost always means better output. Don’t make the AI guess your situation.
- Input Data (15%): Give it the actual raw material. Meeting notes, customer feedback, research reports, whatever’s relevant. The author’s rule is blunt and correct: never assume it knows your situation.
- Quality Checks (4%): Ask the model to review its own work. Flag weak assumptions. Point out what’s missing. This tiny addition catches a surprising number of mistakes before they reach you.
- Constraints (8%): Set the guardrails. Max word count, no jargon, beginner-friendly, a specific budget range. Limits force focus, and focus improves consistency.
- Examples (5%): Show it what “good” looks like. Give an input, the desired output, and the format. According to the expert, examples cut down misinterpretation in a big way.
- Iteration Request (5%): Ask it to improve on itself. “Critique your response.” “Suggest three alternatives.” Prompting works best as a back-and-forth, not a one-shot lottery ticket.
- Instructions (10%): Spell out the exact actions. Analyse. Identify. Prioritise. The person who shared this notes that action verbs do real work, so pick them on purpose.
- Output Format (8%): Tell it the shape you want. Table, bullet points, step-by-step guide, JSON. Models follow structure reliably when you ask for it explicitly.
What I love about this list is how practical it is. You don’t need all 10 in every prompt. But when an answer comes back weak, you now have a checklist to diagnose why. Missing context? No role? No format? You can find the gap in seconds.
Why this actually works
Vague prompts leave the model to fill in a hundred blanks, and it fills them with the most average, most predictable stuff possible. That’s the “filler” the original poster kept getting. Every component you add removes a blank and points the AI at your specific problem instead of the generic version of it.
Structure isn’t extra effort. It’s the difference between a search-bar answer and a real deliverable.
A quick way to start today
You don’t have to memorize the whole framework. The creator’s own path started with just two moves, and I’d suggest the same:
- Add a role and clear objective to your next prompt. Watch how much sharper the reply gets.
- Then layer in context and an output format. These four alone cover most of the quality jump.
- Once that feels natural, start using the iteration request to refine instead of restarting.
Build the habit one piece at a time and it becomes automatic. Soon you’ll be writing structured briefs without thinking about it, and your “average” AI results will quietly disappear.
The author closed with a question I’ll pass along, because it’s a good gut check: are you a structured prompt writer, or still winging it? No shame either way. I was firmly in the “winging it” camp not long ago.
If prompting is part of your daily work, this breakdown is worth saving and worth sharing with anyone on your team still getting mediocre output from AI. Check out the full LinkedIn post for the complete infographic with an example for every single component.