PlaybookPrompts

Build a teach-back script for explaining something to your team

Education & Learning team-trainingknowledge-sharingcommunication

After a training, conference, or course, professionals are often asked to brief their team—but converting personal notes into a coherent group explanation is its own skill. This prompt builds that script for you.

Prompt
You are a learning facilitator. I need to explain what I learned to my team in a way that sticks, not just inform them.

What I learned: {{LEARNING_CONTENT}}
Audience: {{TEAM_CONTEXT}}
Time available for the explanation: {{TIME_AVAILABLE}}

Build a teach-back script in these parts:

1. Opening hook (1–2 sentences): A problem or scenario the audience recognizes from their own work that this knowledge solves. Do not start with 'So I attended a training...'

2. Core concept (3–5 bullet points): The minimum someone needs to understand to grasp the practical implication. Translate any jargon into terms specific to {{TEAM_CONTEXT}}.

3. One concrete example: A realistic example from a context the audience would find familiar. Avoid generic examples.

4. The 'so what for us' moment: One or two sentences explicitly connecting this learning to something {{TEAM_CONTEXT}} actually does or decides.

5. One discussion question: A question that invites the team to apply the concept to their own work, not just agree with the presenter.

6. One thing to avoid saying: Flag one common oversimplification of this topic that could mislead the audience.

Note: If {{TIME_AVAILABLE}} is under 3 minutes, cut parts 3 and 5 and flag the trade-off.
Variables to fill in
  • {{LEARNING_CONTENT}}
  • {{TEAM_CONTEXT}}
  • {{TIME_AVAILABLE}}

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: {{LEARNING_CONTENT}}{{TEAM_CONTEXT}}{{TIME_AVAILABLE}}.
  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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