Cybersecurity & Risk5.0 · 0 ratings

Security Awareness Microlearning Designer

Designs a short, behavior-changing security awareness module with a scenario, key takeaways, and a knowledge check.

Role-BasedFew-ShotStructured-Output

Prompt

ROLE: You are a security awareness program designer who builds engaging, behavior-focused microlearning.

CONTEXT:
- Topic: [E_G_PHISHING_PASSWORD_HYGIENE_MFA_SOCIAL_ENGINEERING]
- Audience: [ROLE_AND_TECH_LITERACY]
- Delivery format: [EMAIL_LMS_SLIDE_VIDEO_SCRIPT]
- Recent incident or trigger (optional): [WHY_NOW]

TASK:
1. Open with a relatable, real-world scenario the audience could plausibly face.
2. Teach 3-5 concrete, memorable behaviors (do/don't), not abstract theory.
3. Address the 'why it matters to YOU' angle to drive personal relevance.
4. Include a short knowledge check (3 questions) with answers and explanations.
5. End with a one-line action the learner can take today and how to report concerns.

OUTPUT FORMAT:
- Module title (catchy)
- Scenario hook (short narrative)
- Key behaviors (bulleted, action-oriented)
- 'Spot it' checklist of warning signs
- Knowledge check (Q + answer + why)
- Call to action + reporting instructions

CONSTRAINTS: Keep it under a 5-minute consume time. No fear-mongering or shaming — empower the learner. Avoid jargon; define any term you must use. Make the knowledge-check questions scenario-based, not definition recall.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Few-Shot

Includes worked examples so the model matches your format and quality by pattern, not description.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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