Prototyping for Product Discovery
Summary
Prototyping has long been a core practice in product development, with multiple prototype types serving different discovery needs. Historically, the costs and tradeoffs of these prototypes were stable for decades—until the recent emergence of gen-AI–powered prototyping tools.
Tools such as Lovable, Bolt, and Figma Make have dramatically reduced the cost and time required to create prototypes, especially live-data prototypes, which were previously expensive and developer-dependent. This shift is transformative for product discovery—but only if the tools are used for their true purpose.
The Real Purpose of Prototyping
Prototypes are not meant to be production products. Their highest-value use is to help teams discover a successful product.
Discovering a successful product means:
- Identifying a problem worth solving
- Discovering a solution worth building—one that is meaningfully better than existing alternatives
Most products fail not because they are hard to build, but because teams fail to discover a solution that truly works.
The Four Product Risks
A solution is worth building only if it addresses all four product risks:
- Value – Customers want it and will use or buy it
- Usability – Users can easily understand and use it
- Feasibility – The team can realistically build and deliver it
- Viability – It works for the business (costs, legal, security, compliance)
Prototyping is the primary technique for testing these risks before investing in full product delivery.
Fidelity: How Realistic Should a Prototype Be?
Prototype realism (fidelity) has three dimensions:
- Visual fidelity – How real it looks
- Behavioral fidelity – How real it behaves
- Data fidelity – Whether it uses real (live) data or simulated data
The idea of “just enough fidelity” is often misunderstood. The correct level of fidelity depends entirely on which risk you are testing and which stakeholder you are testing with.
Examples:
- Security stakeholders may need low visual fidelity but real data
- Executives may need high visual fidelity but limited behavior
- Legal and compliance reviews may require high fidelity across all dimensions
- Feasibility testing may require no UI at all
From Prototype to Product
Once a solution worth building has been discovered, teams move from:
- Building to learn → Product discovery
- Building to earn → Product delivery
Production systems require reliability, scalability, security, performance, and maintainability—and are typically built with different tools and skills than prototypes.
The Prototype as a Communication Tool (Secondary Purpose)
Prototypes are also powerful for communicating intent. As IDEO’s Tom Kelley put it:
“If a picture is worth a thousand words, then a prototype is worth a thousand meetings.”
However, this is a secondary benefit. Using prototypes only as specs—without testing them—often leads to costly product failures.
Why Prototyping Skills Matter More Than Ever
Leading product companies now evaluate a candidate’s prototyping and testing skills during interviews because this is how great products are actually created.
The barriers to learning these tools have never been lower, making prototyping and testing the core craft of modern product creation—for both traditional and AI-powered products.
Key takeaway: Prototyping exists to reduce product risk, not to shortcut product delivery.