Prepare SME interview questions to capture expert know-how
A structured interview plan that pulls the real decisions, mistakes, and shortcuts out of a subject matter expert, not just theory.
The prompt
Help me prepare to interview a subject matter expert (SME) so I can build training from what they know.
Topic or task: [WHAT THE TRAINING COVERS]
SME's role: [e.g. "senior claims adjuster, 12 years"]
Learners: [WHO WILL TAKE THE TRAINING and their experience level]
Time I have with the SME: [e.g. "two 45-minute sessions"]
What I already have: [existing docs, slides, SOPs, or "nothing"]
Build an interview plan that:
- Focuses on what people must DO, not everything the SME knows.
- Uses cognitive task analysis techniques: walk me through a real recent case, critical decision points, cues they notice, what a novice would miss.
- Surfaces the most common and most costly mistakes, and why people make them.
- Separates "must know" from "nice to know" and "can look up".
- Captures real stories and examples I can turn into scenarios.
Return:
1. A one-paragraph pre-interview note to send the SME (purpose, time, what to bring).
2. Interview questions grouped by phase: warm-up, task walkthrough, decisions and judgment, mistakes and edge cases, wrap-up. Mark the 8 must-ask questions.
3. Follow-up probes for when answers are vague ("It depends" - on what?).
4. A note-taking template: Task step | Decision or cue | Common error | Example or story | Must/Should/Could know.
5. Tips for keeping a talkative SME on track and for validating content afterward.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Learning & Teaching prompts
What to fill in
- WHAT THE TRAINING COVERS
- e.g. "senior claims adjuster, 12 years"
- WHO WILL TAKE THE TRAINING and their experience level
- e.g. "two 45-minute sessions"
- existing docs, slides, SOPs, or "nothing"
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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