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skills/prototyping-pretotyping/resources/template.md
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skills/prototyping-pretotyping/resources/template.md
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# Prototyping & Pretotyping Experiment Template
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## Workflow
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```
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Prototyping Progress:
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- [ ] Step 1: Identify riskiest assumption to test
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- [ ] Step 2: Choose pretotype/prototype approach
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- [ ] Step 3: Design and build minimum test
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- [ ] Step 4: Run experiment and collect data
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- [ ] Step 5: Analyze results and decide
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```
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## Experiment Design Template
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### 1. Assumption to Test
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**Assumption**: [What are we assuming? E.g., "Users will pay $49/mo for AI-powered analytics"]
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**Why risky**: [Why might this be wrong? Impact if wrong?]
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**Risk score**: [Probability wrong (1-5) × Impact if wrong (1-5) = Risk (1-25)]
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### 2. Test Method
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**Approach**: [Pretotype / Paper / Clickable / Coded / MVP]
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**Fidelity choice rationale**: [Why this fidelity level? What question does it answer?]
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**Estimated cost**: [$X or X hours]
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**Timeline**: [X days to build, Y days to test]
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### 3. Success Criteria
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**Primary metric**: [E.g., "10% landing page → sign-up conversion"]
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**Secondary metrics**: [E.g., "50% complete onboarding, 5 min avg session"]
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**Minimum sample**: [n=X users/observations]
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**Decision rule**:
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- **Persevere** (build it): [Metric ≥ X means validated]
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- **Pivot** (change direction): [Metric < Y means assumption wrong]
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- **Iterate** (refine and re-test): [X > Metric ≥ Y means unclear, need more data]
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### 4. Experiment Build
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**What we're building**: [Landing page, paper prototype, working feature, etc.]
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**Components needed**:
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- [ ] [Component 1, e.g., Landing page copy/design]
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- [ ] [Component 2, e.g., Sign-up form]
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- [ ] [Component 3, e.g., Analytics tracking]
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**Fake vs Real**:
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- **Faking**: [What appears real but isn't? E.g., "Buy Now button shows 'Coming Soon'"]
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- **Real**: [What must actually work? E.g., "Email capture must work"]
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### 5. Participant Recruitment
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**Target users**: [Who are we testing with? Demographics, behaviors, context]
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**Sample size**: [n=X, reasoning: qualitative vs quantitative]
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**Recruitment method**: [Ads, existing users, outreach, intercepts]
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**Screening**: [How do we ensure target users? Screener questions]
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### 6. Data Collection Plan
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**Quantitative data**:
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| Metric | How measured | Tool | Target |
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|--------|--------------|------|--------|
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| [Sign-ups] | [Form submissions] | [Google Analytics] | [≥100] |
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| [Conversion] | [Sign-ups / Visitors] | [GA] | [≥10%] |
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**Qualitative data**:
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| Method | N | Questions/Tasks |
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|--------|---|-----------------|
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| [User interview] | [5-10] | [What problem were you trying to solve? Did prototype help?] |
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| [Task observation] | [10] | [Complete checkout, note errors/confusion] |
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### 7. Results
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**Quantitative**:
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| Metric | Target | Actual | Status |
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|--------|--------|--------|--------|
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| [Sign-ups] | [≥100] | [X] | [✓ / ✗] |
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| [Conversion] | [≥10%] | [Y%] | [✓ / ✗] |
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**Qualitative**:
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- **Observation 1**: [E.g., "7/10 users confused by pricing page"]
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- **Observation 2**: [E.g., "All users expected 'Export' feature"]
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- **Quote 1**: [User said...]
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- **Quote 2**: [User said...]
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### 8. Decision
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**Decision**: [Persevere / Pivot / Iterate]
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**Rationale**: [Why? Which criteria met/not met?]
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**Next steps**:
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- [ ] [If Persevere: Build MVP with features X, Y, Z]
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- [ ] [If Pivot: Test alternative approach A]
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- [ ] [If Iterate: Refine prototype addressing issues 1, 2, 3, re-test in 2 weeks]
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**Learnings**:
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1. [What we learned about assumption]
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2. [What surprised us]
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3. [What to test next]
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---
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## Quick Patterns
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### Pretotype Methods
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**Fake Door Test** (Test demand):
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- Build: Landing page "New Feature X - Coming Soon" with "Notify Me" button
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- Measure: Click rate, email sign-ups
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- Example: "500 visitors, 50 sign-ups (10%) → validates demand"
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**Concierge MVP** (Test workflow manually before automating):
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- Build: Manual service delivery (no automation)
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- Measure: Customer satisfaction, willingness to pay, time spent
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- Example: "Manually curate recommendations for 10 users → learn what good looks like before building algorithm"
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**Wizard of Oz** (Appear automated, human-powered):
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- Build: UI looks automated, humans behind scenes
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- Measure: User perception, task success, performance expectations
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- Example: "Chatbot UI, humans answering questions → test if users accept chatbot interaction before building NLP"
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**Single-Feature MVP** (Test one feature well):
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- Build: One core feature, ignore rest
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- Measure: Usage, retention, WTP
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- Example: "Instagram v1: photo filters only → test if core value enough before building stories/reels"
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### Prototype Methods
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**Paper Prototype** (Test workflow):
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- Build: Hand-drawn screens on paper/cards
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- Test: Users "click" on paper, swap screens, observe
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- Measure: Task completion, errors, confusion points
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- Example: "10 users complete checkout, 3 confused by shipping step → redesign before coding"
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**Clickable Prototype** (Test UI/UX):
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- Build: Interactive mockup in Figma/InVision (no real code)
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- Test: Users complete tasks, measure success/time
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- Measure: Completion rate, time, errors, satisfaction
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- Example: "20 users, 85% complete task <3 min → validates flow"
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**Coded Prototype** (Test feasibility):
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- Build: Working code, limited features/data
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- Test: Real users, real tasks, measure performance
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- Measure: Latency, error rate, scalability, cost
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- Example: "Search 10K docs <500ms → validates approach, ready to scale to 10M docs"
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### Measurement Approaches
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**Quantitative (n=100+)**:
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- Conversion rates (landing page → sign-up, sign-up → payment)
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- Task completion rates (% who finish checkout)
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- Time on task (how long to complete)
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- Error rates (clicks on wrong element, form errors)
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**Qualitative (n=5-10)**:
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- Think-aloud protocol (users narrate thought process)
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- Retrospective interview (after task, ask about confusion/delight)
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- Observation notes (where they pause, retry, look confused)
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- Open-ended feedback (what worked, what didn't)
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**Behavioral > Opinions**:
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- ✓ "50 clicked 'Buy', 5 completed payment" (behavior)
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- ❌ "Users said they'd pay $99" (opinion, unreliable)
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---
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## Quality Checklist
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- [ ] Assumption is risky (high probability wrong × high impact if wrong)
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- [ ] Fidelity matches question (not overbuilt)
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- [ ] Success criteria set before testing (no moving goalposts)
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- [ ] Recruited real target users (not friends/family)
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- [ ] Sample size appropriate (n=5-10 qualitative, n=100+ quantitative)
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- [ ] Measuring behavior (clicks, conversions), not just opinions
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- [ ] Clear decision rule (persevere/pivot/iterate thresholds)
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- [ ] Results documented and shared with team
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