PlaybookPrompts

Rebuild a confusing explanation with proper learning scaffolding

Education & Learning explanationscaffoldingcomprehensionself-directed-learning

Sometimes you've re-read an explanation three times and still don't have it. This prompt diagnoses why it's confusing and rebuilds it with the prerequisite knowledge and structure that was missing.

Prompt
The following explanation of {{CONCEPT}} is not landing for me. Here it is:

{{CONFUSING_EXPLANATION}}

My background: {{MY_BACKGROUND}} (describe relevant knowledge you do and don't have).

1. Diagnose why this explanation is likely failing for someone with my background. Be specific — identify the exact moment or sentence where it assumes knowledge I don't have, skips a logical step, or uses jargon without grounding it.

2. List the prerequisite concepts I need to understand before the explanation can work. For each prerequisite:
   - Define it in plain language in 1-2 sentences.
   - Explain how it connects to {{CONCEPT}}.

3. Rewrite the explanation from scratch, building from the prerequisites. The rewrite should:
   - Define every term before using it.
   - Introduce one idea at a time.
   - Use a concrete example drawn from {{MY_BACKGROUND}} or an everyday context I'm likely familiar with.

4. End with a one-paragraph summary that I could use to explain {{CONCEPT}} to a colleague from memory.

If the original explanation is actually correct and clear, and the issue is a specific prerequisite I'm missing rather than a flaw in the explanation, tell me that directly rather than rewriting unnecessarily.
Variables to fill in
  • {{CONCEPT}}
  • {{CONFUSING_EXPLANATION}}
  • {{MY_BACKGROUND}}

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: {{CONCEPT}}{{CONFUSING_EXPLANATION}}{{MY_BACKGROUND}}.
  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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