Open your chat history and look at the last “plan” or “strategy” you asked an AI to generate. Does it actually tell you what to do, or is it just a list of vague buzzwords like “leverage synergies” and “optimize workflows”?
We see this all the time. You ask for a roadmap, and you get a generic list that sounds smart but offers zero instructions on how to actually start. I ran across a brilliant solution for this exact problem recently. The original poster, u/promptoptimizr on Reddit, shared a technique they call an “Execution Filter.”
This creator was tired of getting strategy with no implementation depth. They realized that unless you force the model to stop being abstract, it will default to safe, high-level nonsense. So, they built a structural layer to force concrete details.
Here is the exact prompt structure the author shared:
The Execution Filter
<Execution_Filter>
The Strategy: Provide the high level conceptual framework.
The Tactical Map: Translate Phase 1 into concrete, measurable actions with defined metrics for success.
The Reality Check: Identify the 3 most likely points of failure in this specific implementation.
Constraint: No abstract advice. Every point must have a measurable action attached.
</Execution_Filter>
⚙️ Why This Works
This isn’t just a request for information; it’s a logic gate. Here is why this specific structure gets better results than a standard query.
- The XML Tags: By wrapping the instructions in <Execution_Filter> tags, the author clearly demarcates the instruction set from the rest of the conversation. This helps the model treat these rules as a persistent system instruction rather than just conversational fluff.
- Forced Decomposition: The prompt forces the AI to separate “Strategy” (the what) from the “Tactical Map” (the how). LLMs often blend these two, resulting in a muddy middle ground. By explicitly asking for a “Tactical Map” with “measurable actions,” the user forces the model to switch from a creative writing mode to a logical planning mode.
- The Reality Check (The Secret Sauce): This is the most powerful part of the prompt. The author asks the model to “Identify the 3 most likely points of failure.” AI models are naturally sycophantic; they want to please you. They rarely offer criticism unless explicitly told to do so. By demanding a prediction of failure, you force the model to simulate the plan, look for holes, and report them. This process, often called a “pre-mortem” in project management, grounds the AI’s hallucination in reality. If it can see where the plan breaks, the plan itself becomes more realistic.
- Negative Constraints: The final line, “No abstract advice,” acts as a negative constraint. It cuts off the model’s tendency to use filler words. If a point doesn’t have a verb and a metric, this constraint tells the model to discard it.
🧪 Variations to Try
I think this filter is a great base, but you can tweak it for different scenarios. Here are two ways you might adapt the author’s work:
Variation A: The Resource Auditor
If you are working with a budget or a team size limit, add a line to the filter:
“Resource Audit: For every tactical step, list the estimated time required and the specific role/person responsible.”
This prevents the AI from suggesting massive projects that require a 50-person team when you are a solo founder.
Variation B: The First 48 Hours
Sometimes the hardest part is just starting. Add this line:
“Immediate Action: What are the first 3 physical steps to take in the next 48 hours to initiate Phase 1?”
This forces the model to zoom in from the “Tactical Map” to the immediate present.
💡 Final thought
The original creator noted that manually filtering every request is a chore, but the jump in quality is undeniable. When you force the AI to predict its own failure, you stop getting a yes-man and start getting a business partner.
Go give this a shot on your next project planning session!
Check out the original discussion here.
My Execution Filter for killing theoretical fluff
by u/promptoptimizr in ChatGPTPromptGenius