Skill Guide

Prototyping AI-powered experiences using Figma, Framer, or code-based tools (React, Vercel AI SDK)

The discipline of rapidly creating functional, interactive mockups of AI-powered user interfaces and workflows to validate concepts, test user interactions, and communicate design intent before full-scale development.

This skill drastically reduces development risk and cost by front-loading user validation and technical feasibility checks on AI features. It enables organizations to iterate on complex AI interactions, secure stakeholder buy-in with tangible demos, and accelerate time-to-market for intelligent products.

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9.0 Avg Demand

25% Avg AI Risk

How to Learn Prototyping AI-powered experiences using Figma, Framer, or code-based tools (React, Vercel AI SDK)

Focus on: 1) Core UX/UI principles for conversational and generative interfaces (e.g., managing user expectations, loading states, error handling). 2) Basic tool proficiency: Figma's prototyping features for static click-throughs, Framer's drag-and-drop animation and component logic. 3) Understanding fundamental AI API patterns (e.g., request/response cycles for a text generation endpoint).

Move from static to interactive prototypes. Use Figma's variables and interactive components for branching logic based on simulated AI outputs. Leverage Framer's code components to fetch data from a mock API. In code, build a simple Next.js page with the Vercel AI SDK to stream a hardcoded response. Common mistake: Over-polishing visual design before validating the core interaction loop.

Architect complex, stateful prototypes that mirror production system constraints. Design for latency, fallback states, and user correction flows. Use the Vercel AI SDK with function calling to prototype tool-use scenarios. Align prototypes with technical architecture (e.g., simulating edge function behavior) and data schemas. Mentor teams on balancing fidelity with speed and identifying the right prototyping fidelity for each project stage.

Practice Projects

Beginner

Project

Conversational Search Assistant Mockup

Scenario

Design a UI where a user asks a natural language question about a company's internal knowledge base and receives a synthesized answer with sources.

How to Execute

1. In Figma, create frames for the empty state, user query input, loading skeleton, and final answer with citations. 2. Use Figma's prototyping to connect these frames sequentially to simulate a linear flow. 3. Add a simple animation on the loading skeleton to indicate processing. 4. Test the static click-through with a user to gather feedback on clarity.

Intermediate

Project

Interactive Multi-Step Form with AI Validation

Scenario

Prototype a user onboarding flow where an AI reviews a user's answers to multiple questions and provides real-time, contextual feedback before submission.

How to Execute

1. In Framer, build the form using input components. 2. Create a 'validation' function that checks inputs against predefined rules (simulating AI logic). 3. Use Framer's 'on change' events to trigger this function and display contextual tooltip errors or success messages dynamically. 4. Implement a submit action that shows a summary 'AI analysis' of the entire form.

Advanced

Project

Code-Based Prototype for Generative Document Editing

Scenario

Build a functional prototype of a document editor where users can highlight text and use an AI to rewrite, summarize, or change tone, with real-time streaming of the revised text.

How to Execute

1. Scaffold a Next.js application. 2. Implement a rich text editor (e.g., Tiptap). 3. Use the Vercel AI SDK's `useChat` or `useCompletion` hook to connect a selected text passage to a backend API route. 4. The API route streams a mocked, pre-defined AI response back to the client, replacing the original text in the editor. 5. Implement UI for loading states, cancelation, and undo/redo to handle the AI interaction cycle.

Tools & Frameworks

Software & Platforms

Figma (with Variables & Advanced Prototyping)Framer (with Code Components & Page Logic)Next.js / ReactVercel AI SDK (useChat, useCompletion, useObject)Vercel (for deployment & serverless functions)

Use Figma for high-fidelity, collaborative UI mockups with basic interactivity. Use Framer for rapid, interactive prototypes with animation and logic that can be deployed. Use React/Next.js with the Vercel AI SDK for high-fidelity, functional code prototypes that closely simulate production AI interactions, especially streaming and tool use.

Design & Development Patterns

Conversational UI PatternsStreaming Response UISkeleton Loading StatesFunction Calling/Tool Use VisualizationError & Fallback State Design

Apply these patterns to solve specific interaction challenges: streaming patterns for managing perceived latency, tool-use visualization to explain AI actions, and robust error states to handle AI's non-deterministic nature gracefully.

Interview Questions

Answer Strategy

The candidate must demonstrate understanding of UX for latency and non-determinism. Strategy: Structure the answer around pre-interaction (setting expectations), during-interaction (providing progress), and post-interaction (handling results) states. Sample answer: 'I design a multi-layered approach. First, I set clear expectations with microcopy like 'This may take a moment.' During the wait, I use an indeterminate progress indicator, not a spinner, paired with subtle, meaningful animations that relate to the task, such as a document compiling animation for a summary task. For longer waits (>3s), I provide a skeleton UI that previews the result structure. Post-interaction, I ensure the final result has a clear 'success' state and, crucially, an easy 'retry' or 'edit' path if the AI output isn't satisfactory.'

Answer Strategy

Tests the candidate's foresight on AI's non-deterministic nature and user-centric design for trust. Strategy: The answer must show proactive design for uncertainty and graceful degradation. Sample answer: 'On a project prototyping an AI-powered contract review tool, the model could miss clauses. In my Figma prototype, I designed a 'confidence indicator' next to key findings and included a mandatory user verification step before any action could be taken. I also built a 'Show Sources' interaction that linked AI claims back to highlighted text in the original document. This taught users to view the AI as a co-pilot, not an authority, which was critical for internal validation of the concept.'