ThinkWithPrompt by SynotticThinkWithPrompt

Break a long course into a microlearning series

Splits dense training into short, standalone lessons sequenced over time, each with one objective and a practice moment.

Learning & TeachingFew-ShotStandardEducationPlanFill-in Template

The prompt

Turn my long course or content into a microlearning series. Source content: [PASTE OUTLINE, SLIDES TEXT, OR DOCUMENT] Learners and where they will consume it: [e.g. "retail staff on phones between shifts"] Business outcome: [WHAT SHOULD IMPROVE] Delivery channel: [LMS, mobile app, email, Teams/Slack, video] Time available per lesson: [e.g. "3-5 minutes"] Release schedule: [e.g. "two per week for 6 weeks"] Rules: - One objective per lesson. If a lesson needs two, split it. - Each lesson must stand alone (someone can find it later via search) but the series should build. - Every lesson ends with a quick action or retrieval question, not just content. - Cut nice-to-know material ruthlessly; list what I cut so I can decide. - Microlearning is a poor fit for complex skills needing extended practice; flag any content that should stay as a longer session. Return: 1. A series map table: # | Lesson title | Single objective | Format (video, card, scenario, quiz, infographic) | Key message | Practice or action | Minutes. 2. Release sequence with spaced review lessons built in. 3. A sample lesson written in full so I can see the pattern. 4. Content I cut, and content that should not be microlearning. 5. How to measure whether the series is working beyond views.

Highlighted [brackets] are blanks to replace with your own details.

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What to fill in

  • PASTE OUTLINE, SLIDES TEXT, OR DOCUMENT
  • e.g. "retail staff on phones between shifts"
  • WHAT SHOULD IMPROVE
  • LMS, mobile app, email, Teams/Slack, video
  • e.g. "3-5 minutes"
  • e.g. "two per week for 6 weeks"

How this prompt is built

This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.

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