Build a disposable clickable interface from a verified flow
Turn a verified task flow into a stable, disposable clickable prototype that can be tested without being mistaken for production software.
Why
The workflow problem
Designers need a realistic interaction to test before production engineering, but generated interfaces can hide missing states, unsafe dependencies, and unstable behaviour.
AI can rapidly scaffold a disposable interface and explore alternate implementations. The speed trade-off is that generated code and visual completeness can falsely imply accessibility, security, or technical readiness.
Concrete output
What you will produce
A versioned clickable prototype, build brief, known-limitations list, test scenario, and fixed test-build reference.
How
Run the workflow
Freeze the verified flow and boundaries
The designer records the target task, entry and exit conditions, acceptance boundaries, required states, accessibility expectations, and excluded functionality. A product or technical owner confirms that no production data or credentials are needed.
Generate a disposable scaffold
Ask the approved tool to create only the agreed screens and states with fictional data. Treat the result as a scaffold and record tool, prompt version, generated dependencies, and build date.
Generate a bounded prototype scaffold
Create a disposable clickable prototype for the flow below. Use fictional data only. Task flow and acceptance boundaries: [insert] Required screens and states: [insert] Accessibility requirements: [insert] Explicit exclusions: [insert] Return the implementation plus a list of generated dependencies, unimplemented states, and assumptions. Do not add authentication, analytics, external calls, production credentials, or features outside the brief.Review states and limitations
Designer and technical reviewers inspect content, empty and error states, focus order, contrast, responsiveness, dependencies, and data handling. They remove unsafe elements and document limitations that participants must not infer away.
Lock the test build
The prototype owner creates a fixed version and links it to one test scenario. Changes during the research round require a new version and a note explaining what changed and why.
Human–AI partnership
Who contributes what
AI contribution
Rapidly scaffold interface or code alternatives from explicit boundaries. Generated output is unreviewed implementation material, not production-ready software.
Human responsibility
Own the flow, content, accessibility, security, dependency review, versioning, participant briefing, and test readiness.
Stop rule
Stop AI use if sensitive data or credentials are requested, generated dependencies remain unreviewed, critical states are missing, or the build changes during an active test round.
Check before use
Watch out
Do not let a smooth happy path hide exclusion or failure. Check generated copy, keyboard use, assistive-technology implications, privacy, licensing, and whether participants could mistake the prototype for a live service.
Evidence base
What supports this workflow
One real-project case study and one side-by-side practitioner experiment support fast AI-assisted UI prototyping while documenting pitfalls and variability. They do not establish general quality, accessibility, or engineering safety; the workflow requires review before use in research.
- Li, Maheshwari and Voelker · User-Centered Design with AI in the Loop: Rapid UI Prototyping with Vibe Coding
Real deployed-project case study reporting benefits and pitfalls of rapid UI prototyping with vibe coding.
- Government Digital Service · Using prototypes in user research
Canonical practical guidance for using prototypes to learn rather than treating them as finished products.
- Xinran Ma · I ran the same prompt through three popular AI prototyping tools
Single-tester comparison illustrating output variation across AI prototyping tools; not an independent benchmark.