Validation Experiments & Hypotheses
Transform assumptions into testable hypotheses with lean experiments
🎯 Critical Hypotheses Framework
Each hypothesis follows our structured format: We believe [target users] will [action] if we [solution] when we see [measurable outcome].
🔴 Hypothesis #1: Problem Severity (CRITICAL)
Will actively seek a dedicated prompt management solution
If we provide organization, versioning, and testing capabilities in one platform
We will know this is true when we see 65%+ confirm this as a top-3 pain point AND 7%+ landing page conversion
✅ Scattered prompt storage complaints in AI communities
✅ No clear market leader in prompt management
❓ No direct user interviews yet
Confidence: 60% (based on community signals)
Test Cost: $800 + 25 hours
🔴 Hypothesis #2: Solution Fit (CRITICAL)
Will prefer an integrated solution over stitching together multiple tools
If we provide version control + testing + analytics in one workflow
We will know this is true when we see 75%+ rate prototype as "useful" or "very useful" AND 8+ NPS
🔴 Hypothesis #3: Willingness to Pay (CRITICAL)
Will pay $19/month for individual Pro accounts
If we demonstrate 5+ hours saved per month through automation
We will know this is true when we see 40%+ accept price in surveys AND 15+ pre-orders at $19
🟡 Hypothesis #4: Team vs Individual Priority (HIGH)
Will prioritize collaboration features over advanced individual features
If we provide shared libraries and approval workflows
We will know this is true when we see team accounts have 3x higher LTV than individual
🟢 Hypothesis #5: Multi-Model Testing Value (MEDIUM)
Will find side-by-side comparison more valuable than single-model optimization
If we provide unified testing interface across OpenAI, Anthropic, Google
We will know this is true when we see multi-model tests are 60%+ of all test executions
🧪 Experiment Catalog
Experiment #1: Problem Discovery Interviews
Hypothesis Tested: #1 (Problem Severity)
Method: Semi-structured interviews with AI engineers and prompt engineers
Setup:
- Recruit 25 practitioners via LinkedIn, AI Discord servers, Reddit r/MachineLearning
- Offer $75 Amazon gift card incentive
- 45-minute video calls using structured interview guide
- Focus on current prompt management pain points and workflows
Timeline: 3 weeks
Cost: $1,875 (incentives)
Success: 65%+ confirm problem
Owner: Founder
Experiment #2: Landing Page Smoke Test
Hypothesis Tested: #1 (Problem Severity) + #2 (Solution Interest)
Method: Landing page with waitlist signup + A/B test headlines
Variant B: "Version control for prompts. Finally."
Variant C: "The prompt manager your AI team needs"
Drive 2,000+ visitors via Google Ads targeting "prompt engineering" keywords
Timeline: 2 weeks
Cost: $1,200 (ads)
Success: 7%+ signup rate
Owner: Founder
Experiment #3: Wizard of Oz MVP
Hypothesis Tested: #2 (Solution Fit) + #3 (Willingness to Pay)
Method: Manually deliver core features to simulate the product
Setup:
- Create Google Form to collect prompts and organize requests
- Build simple Notion workspace per user with organized prompt library
- Manually test prompts across OpenAI/Anthropic and provide comparison reports
- Deliver "analytics" via spreadsheet showing performance metrics
- Ask for payment ($19) after 2-week trial period
Timeline: 4 weeks
Cost: $400 (LLM API costs)
Success: 8+ satisfaction, 40%+ pay
Owner: Technical co-founder
| Experiment | Hypothesis | Impact | Effort | Priority |
|---|---|---|---|---|
| Discovery Interviews | #1 Problem Severity | 🔴 Critical | Medium | 1 |
| Landing Page Test | #1, #2 | 🔴 Critical | Low | 2 |
| Wizard of Oz MVP | #2, #3 | 🔴 Critical | High | 3 |
| Van Westendorp Pricing | #3 Pricing | 🟡 High | Low | 4 |
| Pre-Order Campaign | #3 Willingness to Pay | 🟡 High | Medium | 5 |
| Multi-Model Testing | #5 Feature Value | 🟢 Medium | Medium | 6 |
📅 8-Week Validation Sprint
Weeks 1-2
Problem Validation
Weeks 3-4
Solution Testing
Weeks 5-6
Pricing & Payment
Weeks 7-8
Synthesis & Decision
🎯 Week 1-2: Problem Validation
🧪 Week 3-4: Solution Testing
💰 Week 5-6: Pricing & Willingness to Pay
✅ Go/No-Go Success Criteria
| Category | Metric | Must Achieve (GO) | Stretch Goal |
|---|---|---|---|
| Problem | Interview confirmation rate | 65%+ | 80%+ |
| Problem | Landing page signup rate | 7%+ | 12%+ |
| Solution | Prototype satisfaction (1-10) | 7.5+ | 8.5+ |
| Solution | Net Promoter Score | 30+ | 50+ |
| Pricing | Willingness to pay $19/month | 40%+ | 60%+ |
| Pricing | Actual pre-orders collected | 15+ | 30+ |
| Overall | Critical hypotheses validated | 3/3 | 5/5 |
✅ GO DECISION
All "Must Achieve" criteria met
⚠️ CONDITIONAL GO
70%+ criteria met, clear path to remainder
❌ NO-GO
<70% criteria met, no clear fixes
🔄 Pivot Triggers & Contingency Plans
🚨 Trigger #1: Problem Doesn't Exist
Signal: <50% of users confirm prompt management as significant pain
Action: Deep-dive interviews on actual workflow pain points
Pivot Options:
- AI workflow automation for developers
- Model comparison/evaluation platform
- AI cost optimization tools
🚨 Trigger #2: Solution Doesn't Fit
Signal: <60% satisfaction with integrated approach
Action: Test individual components (just versioning, just testing)
Pivot Options:
- Focus on single-feature tool (e.g., prompt testing only)
- Browser extension for prompt capture
- API-first prompt management service
⚠️ Trigger #3: Price Resistance
Signal: Acceptable price <$10/month for most users
Action: Test freemium model with usage-based upsells
Pivot Options:
- Free tool with premium prompt marketplace
- Enterprise-only with higher price point
- Usage-based pricing (per test execution)
⚠️ Trigger #4: Individual vs Team Mismatch
Signal: Teams show low interest, individuals love it (or vice versa)
Action: Double-down on segment showing strong signal
Pivot Options:
- Pure B2B team collaboration focus
- Consumer creator tools for prompt sharing
- Developer-focused CLI/API tools
📋 Experiment Documentation Template
Date: [Start - End]
Hypothesis Tested: #X
### Setup
- Method: [What we did]
- Sample: [Size and criteria]
- Tools: [Platforms used]
- Cost: [$X]
### Results
| Metric | Target | Actual | Pass/Fail |
|--------|--------|--------|-----------|
### Key Learnings
- [Insight #1]
- [Surprise finding]
- [User quote]
### Next Steps
- [Product implications]
- [Follow-up experiments]