Concept·Prototyping·Augmentation·Developing·CON-021
Prompt-to-Prototype
Value hypothesis
Produces functional, interactive prototypes without manually wiring hotspots or engineering support, achieving higher fidelity faster.
Innovation · Velocity
The designer describes interaction behaviors, user flows, or screen states in natural language, and the AI tool or coding assistant generates a functional code prototype.
Risks in application
Black Box Rationale
Prompted prototypes contain decisions no one can account for or explain, making designs less internally defensible and more difficult to test.
Constraint Blindness
Creates code the designer cannot read, interrogate, or explain; engineering teams may inherit unusable code.
Homogenization
Everything looks like Tailwind.
Expertise that differentiates
Interaction Design
Specifying interaction behaviors, transition logic, edge cases, and error states with the precision needed to prompt AI effectively and evaluate whether outputs match design intent.
Business Framing
Selecting what is worth prototyping, calibrating scope against the stakeholder questions that need answering, and preventing work tests the wrong hypotheses.
Technical Feasibility
Assessing code suitability for production; recognising interaction patterns or behaviors that would be impractical to replicate at scale.
AI Fluency that assures
Transparency Diligence
Being honest about which prototypes have been properly vetted and which are just drafts.
Deployment Diligence
Understanding limits and risks of prototype-to-code pipelines; assuring exploratory work is not transferred into production.
Related
Possible Indicators
New method adoption
Usage of functional code-based prototypes without engineering support
Cycle time compression
Elapsed time from specification to testable prototype relative to engineering-supported prototype baseline
Sources
Batchu (n.d.). Reimagining prototyping with AI. UX Design (Medium).
Borg et al. (2025). Vibe Coding and the New Prototyping Playbook. IEEE Software.
Baldwin (2026). Prototyping for the unknown. Into Design Systems AI Conference 2026.
Six (2026). Building real design systems with agents. Into Design Systems AI Conference 2026.
Wickes (2025). Prototyping with AI. IDEO U.
Teeters, R. (2026). Concept to code: Transforming ideas into functional products with Kiro [conference talk]. Designing with AI 2026, Rosenfeld.
Dhoosche, C. (2026). Coordinating chaos: Preventing workflow fragmentation when everyone accelerates with AI [conference talk]. Designing with AI 2026, Rosenfeld.
Lowson, A. (2026). Rehashing the Double Diamond: collaborating across functions with AI-assisted prototyping [conference talk]. Designing with AI 2026, Rosenfeld.
Chapelle, B. (2026). Leading through ambiguity: Supporting a design team relearning their craft [conference talk]. Designing with AI 2026, Rosenfeld.
Sony, K. (2026). Moving AI offscreen: Exploring failures, constraints, and resourcefulness [conference talk]. Designing with AI 2026, Rosenfeld.
Ford, P. (2026). New work, new words: A glossary for AI [conference talk]. Designing with AI 2026, Rosenfeld.