PlaybookPrompts

Stress-test a mental model you use to make decisions

Education & Learning mental-modelscritical-thinkingdecision-makingmetacognition

Mental models only fail you in situations you haven't thought through yet. This prompt systematically finds the edges of a model you rely on and tells you when to stop using it.

Prompt
I regularly use the mental model of {{MENTAL_MODEL}} to make decisions in my work as a {{JOB_ROLE}}. Here is how I currently understand and apply it:

{{MY_CURRENT_UNDERSTANDING}}

1. Evaluate my current understanding. Identify:
   a. What I've described correctly.
   b. Any subtle errors, oversimplifications, or missing nuance in my description.
   c. Any vocabulary I've used imprecisely.

2. Describe the original context in which {{MENTAL_MODEL}} was developed or is most valid. What assumptions does it rely on being true?

3. Generate 4 specific scenarios from the world of {{JOB_ROLE}} where applying {{MENTAL_MODEL}} would lead to a poor decision. For each scenario:
   - Describe the situation briefly.
   - Explain why the model breaks down here.
   - Name a different mental model or approach that would serve better in that situation.

4. Generate 3 scenarios where {{MENTAL_MODEL}} is exactly the right tool and outperforms intuition or other common approaches.

5. Write a one-sentence 'warning label' for {{MENTAL_MODEL}} — a crisp reminder of when not to use it — that I could keep in my notes.

If {{MENTAL_MODEL}} is ambiguous or goes by different names in different fields, ask me to clarify before proceeding rather than guessing which version I mean.
Variables to fill in
  • {{MENTAL_MODEL}}
  • {{JOB_ROLE}}
  • {{MY_CURRENT_UNDERSTANDING}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{MENTAL_MODEL}}{{JOB_ROLE}}{{MY_CURRENT_UNDERSTANDING}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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