PlaybookPrompts

Generate a realistic case study to practice applying a new concept

Education & Learning case-studyapplied-learningpractice

Reading about a framework and applying it are different skills. This prompt creates a realistic workplace case study tailored to your industry so you can practice applying a concept before you need it in the real world.

Prompt
You are a professional training designer. I recently learned {{CONCEPT}} and want to practice applying it before I encounter it in a live situation.

My industry: {{INDUSTRY}}
My role: {{MY_ROLE}}

Create one realistic case study in these parts:

Part 1 — Situation (150–200 words):
Describe a plausible scenario from {{INDUSTRY}} that a person in {{MY_ROLE}} would actually face. Do not mention {{CONCEPT}} by name. Write it as a situation briefing, not a textbook example.

Part 2 — Decision point:
End with one specific decision or question the person in the scenario must answer. Make it concrete (a choice between options, a recommendation to write, a diagnosis to make).

Part 3 — Evaluation rubric (reveal only after I submit my answer):
List 4–6 criteria I should have considered when applying {{CONCEPT}}. Note which criteria are often missed by people new to this concept.

Instructions for use: After I read Parts 1 and 2, I'll write my response. Only then should you share Part 3 and give me feedback.

Note: If {{CONCEPT}} is a soft skill (e.g., active listening), the case study will assess reasoning about the skill, not the skill itself—flag this limitation.
Variables to fill in
  • {{CONCEPT}}
  • {{INDUSTRY}}
  • {{MY_ROLE}}

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}}{{INDUSTRY}}{{MY_ROLE}}.
  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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