🧪Prototype

NEW

Also known as: MVP, Proof of Concept

An early, simplified version of a system built to test concepts, de-risk assumptions, and gather feedback.

DESCRIPTION

Prototypes validate ideas quickly and cheaply before full-scale development. They vary from low-fidelity mockups to functional slices of working software. In data and AI, prototypes trial data availability, model feasibility, and user value. Success relies on clear hypotheses and criteria for pivot or persevere decisions. Prototypes should be disposable or incrementally evolved based on learnings.

ALTERNATIVE DEFINITIONS

Wikipedia

A prototype is an early sample, model, or release of a product built to test a concept or process.

Highlights early sampling to evaluate concepts.

Source

Wikipedia

A minimum viable product (MVP) is a version of a product with just enough features to be usable by early customers who can then provide feedback for future development.

Emphasizes a feedback-oriented, learn-then-build approach.

Source

RELATED TERMS

Minimum Viable Product Design Sprint Rapid Prototyping User Research A/B Testing Feature Flags Usability Testing Risk Reduction Lean Startup Requirements Validation

COMPETING TERMS

General Availability

KNOWLEDGE AREAS

Software Engineering Platform & Tooling Testing & Validation Collaboration

TARGET AUDIENCE

Software Developer AI Product Manager Data Scientist Data Management Consultant

EXTERNAL SOURCES

• Wikipedia - Prototype
• GOV.UK Service Manual - Guidance for testing prototypes
• Google - Design sprint prototyping methods
• ISO - Systems lifecycle context

TOP QUESTIONS

Why prototype before building the full system?

It validates assumptions and uncovers risks early, saving cost and time. Fast feedback informs better designs.

Category: Relevancy

How does a Prototype differ from an MVP?

A prototype tests concepts and may be disposable; an MVP delivers usable value to early users for feedback.

Category: Misunderstanding

What should a good Prototype include?

Clear hypotheses, success criteria, and a plan to discard or evolve. Instrumentation for learning is essential.

Category: Requirements

What are risks of keeping a Prototype in production?

Shortcuts may cause technical debt and security issues. Refactoring or rebuilding is often necessary.

Category: Risks

How do you test a Prototype effectively?

Recruit target users, run structured tasks, and collect metrics. Iterate rapidly on findings.

Category: Testing

Where do prototypes fit in an agile lifecycle?

They precede or complement early sprints, feeding validated learning into backlog prioritization.

Category: Lifecycle

What tools support software Prototyping?

UI prototyping tools, feature flags, and sandboxes. For data, notebooks and sample datasets are common.

Category: Tooling

How does Prototyping apply to AI models?

Use small datasets and baseline models to test feasibility. Evaluate real-world constraints and ethical risks early.

Category: Application

When should a Prototype be discarded?

If it fails hypotheses, is unsafe, or cannot scale. Document learnings and move on.

Category: Decision