PlaybookPrompts

Map what you don't know about a topic you think you know

Education & Learning knowledge-auditself-assessmentskill-gapsmetacognition

The most dangerous gaps are the ones you don't know you have. This prompt runs a structured audit of your current understanding and returns a prioritized list of knowledge gaps worth closing.

Prompt
I believe I understand {{TOPIC}} at a {{SELF_ASSESSED_LEVEL}} level (beginner / intermediate / advanced). I work in {{PROFESSIONAL_CONTEXT}}.

Run a knowledge-gap audit by doing the following:

1. List 8 questions that a genuine expert in {{TOPIC}} would expect someone at the {{SELF_ASSESSED_LEVEL}} level to be able to answer confidently. Number them.

2. For each question, give me a 'trap answer' — a plausible-sounding but incomplete or wrong answer that someone at my level often gives — so I can check whether that's what I would have said.

3. After the 8 questions, provide full model answers to each one.

4. Identify which 2-3 questions are most likely to represent real gaps for someone in {{PROFESSIONAL_CONTEXT}}, and explain briefly why those gaps cause the most practical problems on the job.

5. For each identified gap, suggest one specific resource type (not a named book or course, since those date quickly — instead describe the type: e.g., 'a worked case study', 'a peer who has done X', 'official documentation for Y') that would close it efficiently.

Be direct if my self-assessed level seems inconsistent with the questions I struggle with. Don't adjust the difficulty down to protect my self-image.
Variables to fill in
  • {{TOPIC}}
  • {{SELF_ASSESSED_LEVEL}}
  • {{PROFESSIONAL_CONTEXT}}

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: {{TOPIC}}{{SELF_ASSESSED_LEVEL}}{{PROFESSIONAL_CONTEXT}}.
  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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