🏴‍☠️ Your Prompt Isn’t The Issue

Structure Beats Vague Asks

Author

Captain YAR
January 20, 2026 • Estimated Reading Time: 8 minutes

Most people treat AI like a slot machine: pull the lever, hope for magic, feel disappointed.
But the fastest way to get great output is to stop guessing and start directing.
I just came across a brilliant guide from a talented LinkedIn creator who lays out a simple system for doing exactly that.
It’s practical, repeatable, and it removes the weird randomness that makes AI feel unreliable.
And once you use it a few times, you’ll wonder why you ever prompted any other way.

The engine behind this method is a shift from “asking” to “modeling.” Instead of describing what you want with fuzzy adjectives, you reverse engineer a real example that already nails the vibe. When the AI can study a “golden sample,” it stops guessing what you mean by phrases like “professional but witty.” It can see the pattern, copy the structure, and reproduce the feel with far less drift.

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The Reverse Engineering Protocol
Step one is simple: do not start prompting until you know your destination. Find a piece of content that makes you say, “Yes. Exactly like that.” The creator recommends saving it as a markdown (.md) file because markdown makes headers, hierarchy, and formatting easier for the model to read than a PDF.

Then you ask the AI to extract the “DNA” of that example instead of copying it. The goal is a compact blueprint you can reuse for new work. Here’s the prompt the author uses for that breakdown phase: 

Analyze this reference so you can recreate something similar later.
Give me a short, actionable blueprint:
- What is it?
- Tone
- Key patterns
Keep it under 100 words total.
These will be your instructions to recreate this type of text as closely as possible without having access to the original reference.
Everything in a codeblock.

Defining the Rules of Engagement
Once you have the style blueprint, you define the logic and the goal with what the creator calls a “Success Brief.” This is where most people skip ahead, and it’s why their outputs feel generic. You answer four things: what you want, how the reader should react, what it should not sound like, and what success actually means.

That “does NOT sound like” line is pure gold. It blocks the robotic clichés before they appear and forces the model to stay in your lane. Even better, defining success as “the reader takes the next step” pushes the AI toward persuasive clarity instead of polite filler.

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The Stack and Iterate Method
Now you combine the blueprint and the success brief into one command, so the AI has both the style map and the outcome target. The creator calls this “stacking,” and it’s the difference between random drafts and consistent results. You also avoid the one-shot trap. Instead of asking for the full deliverable immediately, you tell the AI to plan first, then execute in steps.

Here’s the stacking structure the author shares: 

I uploaded a reference to what I want to achieve. Here’s what makes this reference work:
[Paste your blueprint]
Here’s what I need for my version:
[Paste your success brief]
Now that you know all of this information, let’s create the plan to complete it step by step in a chat (5 steps maximum).
Define the outline, and ask me one question so you can move on to the first step.

Potential Pitfalls to Watch For
Even with a great system, you can sabotage yourself with old habits. The biggest red flag is having no example at all, because then the model is forced to guess your taste. Another is dumping a wall of context without clear constraints, which creates confusion instead of clarity. Vague audience notes like “for experts” also fail because they don’t tell the AI what to say, avoid, or assume. And the final trap is accepting the first draft without steering it, which defeats the purpose of using a chat-based assistant.

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