Turn a specified user flow into an interactive usability-testing prototype without requiring design-tool expertise.
Added Jul 26, 2026
High opportunity (77%)
Product managers often know the interaction they want to test but cannot efficiently build it in Figma. Creating an entire interactive flow is tedious, while open-ended AI? coding tools may invent details and reduce the precise control needed for a valid usability test. Designers can build these prototypes manually, but that consumes skilled time before the concept has been validated.
Build a guided prototyping product that converts structured wireframes, screens, and interaction rules into a test-ready clickable flow. Users retain control over states and navigation instead of relying on an unconstrained prompt. After testing, the validated screens and flow can be exported to Figma for pixel-perfect design work.
AI?-assisted interface generation has made rapid prototype creation practical, but product teams still need precision, predictable flows, and compatibility with established design workflows. The evidence also shows growing willingness among initially resistant designers to use these tools for early validation rather than final design.
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Analyze user behaviour and product analytics (e.g. PostHog, Google Analytics) to identify opportunities to improve product usability and customer experience. Translate user insights into wireframes, mock-ups, high-fidelity designs and interactive prototypes using Figma and AI-assisted design tools.
Partner with product managers, engineers, architects, researchers, and other designers to define user flows, interaction models, and implementation-ready designs. Explore how AI-assisted and agentic workflows can improve user experiences without adding friction or reducing user trust.
Most product teams treat prototypes as miniature success stories. The demo works. The model responds. The interface looks convincing. Everyone leaves the meeting feeling like the idea has momentum. But that can be exactly the wrong outcome. A useful AI prototype shouldn't just prove that something *can* work. It should expose the assumptions most likely to make the product fail. That's the more disciplined view [Goji Labs](gojilabs.com/.../from-idea-to-ai-prototyp...) takes in its recent discussion of AI prototyping: a prototype should test whether the product is worth building, not simply whether the technology is capable of producing an impressive output. **The First Question Shouldn't Be About the Model** A surprising amount of AI product work still starts with questions like: * Which model should we use? * Should this be an agent? * Do we need retrieval? * Can we automate this workflow? * Should we build a chatbot? Those are implementation questions. The more important question is: **What user or business outcome are we actually trying to improve?** If that answer isn't clear, the prototype can become technically interesting without proving much of anything. **Every AI Idea Has a Weak Point** Maybe users don't actually want the feature. Maybe the data is incomplete. Maybe the model isn't reliable enough. Maybe the workflow can't tolerate uncertainty. Maybe the economics are terrible. Maybe the system requires so much human review that the automation doesn't save any work. Goji's recommendation is straightforward: identify the riskiest assumption and design the prototype around testing that first. That's a much better use of prototype budget than polishing the UI around an unanswered fundamental question. **Test the Workflow, Not the Screenshot** AI demos tend to focus heavily on output quality. Does the answer look good? Does the recommendation sou...
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