lyndonkl/prototyping-pretotyping

Prototyping & Pretotyping

Table of Contents

1. Workflow
2. Common Patterns
3. Fidelity Ladder
4. Guardrails
5. Quick Reference

Workflow

Copy this checklist and track your progress:

Prototyping Progress:
- [ ] Step 1: Identify riskiest assumption to test
- [ ] Step 2: Choose pretotype/prototype approach
- [ ] Step 3: Design and build minimum test
- [ ] Step 4: Run experiment and collect data
- [ ] Step 5: Analyze results and decide (pivot/persevere/iterate)

Step 1: Identify riskiest assumption

List all assumptions (demand, pricing, feasibility, workflow), rank by risk (probability of being wrong × impact if wrong). Test highest-risk assumption first. See Common Patterns for typical assumptions by domain.

Step 2: Choose approach

Match test method to assumption and available time/budget. See Fidelity Ladder for choosing appropriate fidelity. Use resources/template.md for experiment design.

Step 3: Design and build minimum test

Create simplest artifact that tests assumption (landing page, paper prototype, manual service delivery). See resources/methodology.md for specific techniques (fake door, concierge, Wizard of Oz, paper prototyping).

Step 4: Run experiment

Deploy test, recruit participants, collect quantitative data (sign-ups, clicks, payments) and qualitative feedback (interviews, observations). Aim for minimum viable data (n=5-10 for qualitative, n=100+ for quantitative confidence).

Step 5: Analyze and decide

Compare results to success criteria (e.g., "10% conversion validates demand"). Decide: Pivot (assumption wrong, change direction), Persevere (assumption validated, build it), or Iterate (mixed results, refine and re-test).

Common Patterns

By assumption type:

Demand Assumption ("People want this"):

• Test: Fake door (landing page with "Buy Now" → "Coming Soon"), pre-orders, waitlist sign-ups
• Success criteria: X% conversion, Y sign-ups in Z days
• Example: "10% of visitors sign up for waitlist in 2 weeks" → validates demand

Pricing Assumption ("People will pay $X"):

• Test: Price on landing page, offer with multiple price tiers, A/B test prices
• Success criteria: Z% conversion at target price
• Example: "5% convert at $49/mo" → validates pricing

Workflow Assumption ("This solves user problem in intuitive way"):

• Test: Paper prototype, task completion with clickable prototype
• Success criteria: X% complete task without help, <Y errors
• Example: "8/10 users complete checkout in <2 minutes with 0 errors" → validates workflow

Feasibility Assumption ("We can build/scale this"):

• Test: Technical spike, proof-of-concept with real data, manual concierge first
• Success criteria: Performance meets targets, costs within budget
• Example: "API responds in <500ms at 100 req/sec" → validates architecture

Value Proposition Assumption ("Customers prefer our approach over alternatives"):

• Test: A/B test messaging, fake door with different value props, competitor comparison
• Success criteria: X% choose our approach over alternative
• Example: "60% choose AI-powered vs manual curation" → validates differentiation

Fidelity Ladder

Choose appropriate fidelity for your question:

Level 0 - Pretotype (Hours to Days, $0-100):

• What: Fake it before building anything real
• When: Test demand, pricing, value prop assumptions
• Methods: Landing page with sign-up, fake door test, manual concierge, video mockup
• Example: Dropbox video showing product before building it (3-4 min video, 70K→75K sign-ups overnight)
• Pros: Fastest, cheapest, tests real behavior (not opinions)
• Cons: Can't test workflow/usability in detail, ethical concerns if too deceptive

Level 1 - Paper Prototype (Hours to Days, $0-50):

• What: Hand-drawn sketches, printed screens, index cards
• When: Test workflow, information architecture, screen structure
• Methods: Users "click" on paper, you swap screens, observe confusion points
• Example: Banking app - 10 paper screens, users simulate depositing check, identify 3 workflow issues
• Pros: Very fast to iterate (redraw in minutes), forces focus on structure not polish
• Cons: Can't test real interactions (gestures, animations), feels "fake" to users

Level 2 - Clickable Prototype (Days to Week, $100-500):

• What: Interactive mockups in Figma, InVision, Adobe XD (no real code)
• When: Test user flow, UI patterns, interaction design
• Methods: Users complete tasks, measure success rate/time/errors, collect feedback
• Example: E-commerce checkout - 8 screens, 20 users, 15% abandon at shipping → fix before coding
• Pros: Looks real, easy to change, tests realistic interactions
• Cons: Can't test performance, scalability, backend complexity

Level 3 - Coded Prototype (Weeks to Month, $1K-10K):

• What: Working software with limited features, subset of data, shortcuts
• When: Test technical feasibility, performance, integration complexity
• Methods: Real users with real tasks, measure latency/errors, validate architecture
• Example: Search engine - 10K documents (not 10M), 50 users, <1s response time → validates approach
• Pros: Tests real technical constraints, reveals integration issues
• Cons: More expensive/time-consuming, harder to throw away if wrong

Level 4 - Minimum Viable Product (Months, $10K-100K+):

• What: Simplest version that delivers core value to real customers
• When: Assumptions mostly validated, ready for market feedback
• Methods: Launch to small segment, measure retention/revenue, iterate based on data
• Example: Instagram v1 - photo filters only (no video, stories, reels), launched to small group
• Pros: Real market validation, revenue, learning
• Cons: Expensive, longer timeline, public commitment

Guardrails

Ensure quality:

1. Test riskiest assumption first: Don't test what you're confident about

   • ✓ "Will customers pay $X?" (high uncertainty) before "Can we make button blue?" (trivial)
   • ❌ Testing minor details before validating core value

2. Match fidelity to question: Don't overbuild for question at hand

   • ✓ Paper prototype for testing workflow (hours), coded prototype for testing latency (weeks)
   • ❌ Building coded prototype to test if users like color scheme (overkill)

3. Set success criteria before testing: Avoid confirmation bias

   • ✓ "10% conversion validates demand" (decided before test)
   • ❌ "7% conversion? That's pretty good!" (moving goalposts after test)

4. Test with real target users: Friends/family are not representative

   • ✓ Recruit from target segment (e.g., enterprise IT buyers for B2B SaaS)
   • ❌ Test with whoever is available (founder's friends who are polite)

5. Observe behavior, not opinions: What people do > what they say

   • ✓ "50% clicked 'Buy Now' but 0% completed payment" (real behavior → pricing/friction issue)
   • ❌ "Users said they'd pay $99/mo" (opinion, not reliable predictor)

6. Be transparent about faking it: Ethical pretotyping

   • ✓ "Sign up for early access" or "Launching soon" (honest)
   • ❌ Charging credit cards for fake product, promising features you won't build (fraud)

7. Throw away prototypes: Don't turn prototype code into production

   • ✓ Rebuild with proper architecture after validation
   • ❌ Ship prototype code (technical debt, security issues, scalability problems)

8. Iterate quickly: Multiple cheap tests > one expensive test

   • ✓ 5 paper prototypes in 1 week (test 5 approaches)
   • ❌ 1 coded prototype in 1 month (locked into one approach)

Quick Reference

Resources:

• Quick start: resources/template.md - Pretotype/prototype experiment template
• Advanced techniques: resources/methodology.md - Fake door, concierge, Wizard of Oz, paper prototyping, A/B testing
• Quality check: resources/evaluators/rubric_prototyping_pretotyping.json - Evaluation criteria

Success criteria:

• ✓ Identified 3-5 riskiest assumptions ranked by risk (prob wrong × impact if wrong)
• ✓ Tested highest-risk assumption with minimum fidelity needed
• ✓ Set quantitative success criteria before testing (e.g., "10% conversion")
• ✓ Recruited real target users (n=5-10 qualitative, n=100+ quantitative)
• ✓ Collected behavior data (clicks, conversions, task completion), not just opinions
• ✓ Results clear enough to make pivot/persevere/iterate decision
• ✓ Documented learning and shared with team

Common mistakes:

• ❌ Testing trivial assumptions before risky ones
• ❌ Overbuilding (coded prototype when landing page would suffice)
• ❌ No success criteria (moving goalposts after test)
• ❌ Testing with wrong users (friends/family, not target segment)
• ❌ Relying on opinions ("users said they liked it") not behavior
• ❌ Analysis paralysis (perfect prototype before testing)
• ❌ Shipping prototype code (technical debt disaster)
• ❌ Testing one thing when could test many (cheap tests run serially/parallel)

When to use alternatives:

• A/B testing: When have existing product/traffic, want to compare variations
• Surveys: When need quantitative opinions at scale (but remember: opinions ≠ behavior)
• Customer interviews: When understanding problem/context, not testing solution
• Beta testing: When product mostly built, need feedback on polish/bugs
• Smoke test: Same as pretotype (measure interest before building)