# AI-Assisted Research — Exercises

## Exercise 1: Theme Extraction Prompt

**Task:** Write a prompt that takes 5 survey responses (open-ended) and outputs: (1) 3–5 themes, (2) one representative quote per theme, (3) a confidence level (High/Medium/Low) for each theme based on how many responses support it.

**Validation:**
- [ ] Prompt includes context (e.g., product domain)
- [ ] Output format is specified (bullets, table, etc.)
- [ ] Confidence is tied to evidence count, not AI certainty
- [ ] Prompt is under 200 words

**Hints:**
1. "You are a product researcher synthesizing user feedback."
2. "For each theme, cite the number of responses that mention it."
3. "Low confidence = 1 response, Medium = 2, High = 3+."

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## Exercise 2: Competitor Feature Matrix

**Task:** Choose a product category (e.g., project management, CRM, note-taking). Write a prompt that produces a 5x4 table: 5 features × 4 products (3 competitors + "Our product"). Include an instruction to note "Unknown" where the AI cannot verify, and to add a "Last verified" caveat.

**Validation:**
- [ ] Table structure is specified
- [ ] "Unknown" handling is explicit
- [ ] Recency/verification caveat is included
- [ ] You could run this and manually verify a few cells

**Hints:**
1. List the 5 features and 4 products explicitly
2. "If you cannot confirm a feature, write 'Unknown (verify)'"
3. "Add a footer: 'Verify pricing and features on official sites'"

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## Exercise 3: Assumption vs Evidence Labels

**Task:** Take an AI-synthesized output (from a previous exercise or a sample). For each insight, label it: [E] Evidence-based (traceable to input data) or [A] Assumption (AI inferred). Write 1–2 sentences explaining one [A] and how you'd validate it.

**Validation:**
- [ ] Every insight has a label
- [ ] At least one [A] is identified
- [ ] Validation approach is concrete (interview question, data source, etc.)

**Hints:**
1. Evidence = "User said X in quote 3"
2. Assumption = "Users likely want Y because..." with no direct quote
3. Validation: "I would ask: 'When you do Z, what frustrates you?'"

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## Exercise 4: Persona Draft + Grounding

**Task:** Use AI to draft a persona for "mid-market sales managers." Then write a "Grounding Checklist": 3 things you would need to validate with real users before using this persona in a PRD. Include one thing the AI likely got wrong or oversimplified.

**Validation:**
- [ ] Persona has at least: role, goals, frustrations, context
- [ ] Grounding checklist has 3 concrete validation steps
- [ ] At least one likely AI error or oversimplification is named

**Hints:**
1. AI personas tend to be "average" — real users have more variance
2. Grounding: "Interview 5 people who match this role"
3. Oversimplification: e.g., "All sales managers care about X" — segment differences?

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## Exercise 5: End-to-End Research Loop

**Task:** Simulate a mini research loop: (1) Paste 5 feedback quotes, (2) Run an AI synthesis prompt, (3) Write 2 interview questions to validate the top theme, (4) Write a one-paragraph "Research Summary" that states what you learned, what's assumed, and what you'd do next.

**Validation:**
- [ ] All 4 steps completed
- [ ] Summary distinguishes learned vs assumed
- [ ] Next steps are specific (not "do more research")

**Hints:**
1. Summary template: "We learned X (from data). We assume Y (needs validation). Next: Z."
2. Next steps: "Interview 3 users about theme A" or "Check analytics for metric B"