Free Comprehensive Playbook

MVP & Solution Design

Build products that learn, not products that fail. The MVP is not a product development strategy—it's a risk reduction strategy.

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MVP and Solution Design - Building Products That Learn

Why MVP Thinking Has Evolved

The traditional MVP is misunderstood. It's not about shipping a "crappy first version"—it's about maximizing learning per dollar spent.

This playbook deconstructs the MVP and reconstructs it through modern lenses:

  • RAT (Riskiest Assumption Testing) - Test what could kill you first
  • MLP (Minimum Lovable Product) - Viable isn't enough; products must delight
  • Hypothesis-Driven Development - Every feature is an experiment with success criteria
  • Pretotyping - Test before you build with Fake Doors and Wizard of Oz

Based on The Lean Startup (Ries), Pretotype It (Savoia), and modern product management practices

The Company That Learns Fastest Wins

In the age of AI, building is cheap. The competitive advantage is learning velocity—how fast you can identify what works and double down, while abandoning what doesn't.

What You'll Learn

From philosophical foundations to practical techniques, this playbook covers the complete framework for building products that learn.

1

Ch 1: Evolution of MVP

Beyond the Build Trap: RAT vs. MVP, MLP, and Hypothesis-Driven Development.

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2

Ch 2: Assumption Mapping

Desirability, Viability, Feasibility: User Story & Impact Mapping.

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3

Ch 3: Pretotyping

Wizard of Oz, Concierge MVP, and Fake Door testing.

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4

Ch 4: Build Decision

Prioritization frameworks, Build vs. Buy, No-Code revolution.

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5

Ch 5: Metrics

North Star Metric, Retention, NPS, and actionable analytics.

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6

Ch 6: Beta Launch

Go/No-Go criteria, Beta testing strategies, Friction logging.

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7

Ch 7: Decision Framework

Analyzing failure, the decision framework, types of pivots.

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8

Conclusion

Building a perfect engine for learning.

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9

Tools

AI-powered tools and recommended resources for MVP & Solution Design.

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Core Components

Five foundational elements for building products that learn

Riskiest Assumption Test

Identify and test the single assumption that, if wrong, kills your entire business before investing in full product development.

Minimum Lovable Product

Go beyond "viable" to create products that users genuinely love and want to share—the foundation of organic growth.

Hypothesis-Driven Development

Treat every feature as an experiment with defined hypotheses, success metrics, and learning objectives.

Pretotyping Arsenal

Master Fake Doors, Wizard of Oz, Mechanical Turk, and other techniques to validate demand before writing code.

Product Metrics

Instrument your product to capture actionable metrics that prove or disprove your hypotheses with statistical rigor.

Key Concepts You'll Master

Practical frameworks and techniques you can implement immediately

Fake Door Tests

Measure demand by advertising features before they exist. Click rates reveal true interest without building anything.

Wizard of Oz

Simulate automation with humans behind the scenes. Test the experience before investing in technology.

Fidelity Spectrum

Choose the right level of polish—from paper prototypes to production code—based on what you're trying to learn.

Feature Prioritization

Use ICE scoring (Impact, Confidence, Ease) to prioritize experiments and maximize learning per sprint.

Stop Building. Start Learning.

LeanPivot.ai provides AI-powered tools for experiment design, hypothesis tracking, and MVP scoping. Build your perfect learning engine.

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Works Cited & Recommended Reading
RAT vs MVP Philosophy
  • 1. Ries, E. (2011). The Lean Startup. Crown Business.
  • 2. "Why RAT (Riskiest Assumption Test) beats MVP every time." LinkedIn
  • 3. "Pretotyping: The Art of Innovation." Pretotyping.org
  • 6. "Continuous Discovery: Product Trio." Product Talk
  • 7. "MVP Fidelity Spectrum Guide." SVPG
Minimum Lovable Product
  • 8. Olsen, D. (2015). The Lean Product Playbook. Wiley.
  • 9. "From MVP to MLP: Why 'Viable' Is No Longer Enough." First Round Review
  • 10. "Minimum Lovable Product framework." Amplitude Blog
Hypothesis-Driven Development
Assumption Mapping
  • 15. Bland, D. & Osterwalder, A. (2019). Testing Business Ideas. Wiley.
  • 16. "Risk vs. Knowledge Matrix." Miro Templates
  • 17. "Identifying Riskiest Assumptions." Intercom Blog
User Story & Impact Mapping
  • 20. Patton, J. (2014). User Story Mapping. O'Reilly Media.
  • 21. Adzic, G. (2012). Impact Mapping. Provoking Thoughts.
  • 22. "Jobs-to-Be-Done Story Framework." JTBD.info
  • 23. "The INVEST Criteria for User Stories." Agile Alliance
  • 24. "North Star Metric Framework." Amplitude
  • 25. "Opportunity Solution Trees." Product Talk
  • 26. Torres, T. (2021). Continuous Discovery Habits. Product Talk LLC.
Pretotyping Techniques
Prioritization Frameworks
Build vs Buy & No-Code
Metrics & Analytics
Launch Operations & Analysis

This playbook synthesizes methodologies from Lean Startup, Design Thinking, Jobs-to-Be-Done, Pretotyping, and modern product management practices. References are provided for deeper exploration of each topic.