PlaybookPrompts

Generate tested analogies for a technical concept you must explain

Education & Learning analogiescommunicationexplanationteaching

Finding the right analogy for a technical concept is hard and usually done on the spot. This prompt pre-generates several options, tests their limits, and helps you pick the right one for your audience.

Prompt
I need to explain {{TECHNICAL_CONCEPT}} to {{AUDIENCE_DESCRIPTION}} who have no background in {{DOMAIN}}.

1. Generate 4 different analogies for {{TECHNICAL_CONCEPT}}. Each analogy should:
   - Draw from a different domain of everyday experience (avoid tech metaphors for a non-tech audience).
   - Be expressible in 2-3 sentences.
   - Be labeled with the audience context it works best for (e.g., 'works well if audience has managed a team', 'works well if audience is familiar with cooking').

2. For each analogy, identify:
   a. Where the analogy holds up well.
   b. Where it breaks down or could mislead if pushed too far.
   c. One follow-up question an attentive listener might ask that the analogy can't answer.

3. Recommend the best analogy for my specific audience ({{AUDIENCE_DESCRIPTION}}) and explain why.

4. Write a 4-sentence verbal explanation of {{TECHNICAL_CONCEPT}} using the recommended analogy, suitable for saying out loud in a meeting.

Note: If {{TECHNICAL_CONCEPT}} resists analogy because precision matters more than intuition (e.g., legal definitions, safety-critical specs), flag this and suggest a different explanation strategy.
Variables to fill in
  • {{TECHNICAL_CONCEPT}}
  • {{AUDIENCE_DESCRIPTION}}
  • {{DOMAIN}}

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: {{TECHNICAL_CONCEPT}}{{AUDIENCE_DESCRIPTION}}{{DOMAIN}}.
  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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