Prototyping & Design AI use cases

Prototyping & DesignMarketing & Content+1

Brand-specific image generation workflow where creators and brands train private Firefly models on their own assets to generate visuals with consistent style, characters, and details.

Outcome: The useful result is a reusable visual-generation system that can produce campaign, concept, and story assets with less drift from the brand’s existing creative language.

Prototyping & DesignMarketing & Content+1

AI-assisted beverage brand buildout workflow spanning brand identity, market strategy, web design, vendor outreach, packaging, and operational planning.

Outcome: They reported reaching a comparable brand-and-launch-readiness point in about four weeks for less than 10% of the cost they believed a traditional agency-led build would have required.

Prototyping & DesignMarketing & Content+1

Brand-kit-to-design workflow where users connect Canva Brand Kit context and generate editable, on-brand visuals from inside ChatGPT.

Outcome: Teams can move from a plain-English request to a branded draft design faster, then open the result in Canva for human editing and polish.

Business OperationsPrototyping & Design+1

Keyword-to-store setup workflow where a merchant describes a store and AI generates complete editable store layouts with copy and imagery.

Outcome: A merchant can move from a rough store idea to several reviewable storefront directions before doing the slower work of editing, brand refinement, product setup, and launch checks.

Prototyping & DesignCoding & Engineering

Terry Lin built Cooper's Corner, a voice-powered Apple Watch and iPhone fitness app, by combining low-tech index-card prototyping with a structured AI coding workflow in Cursor and Xcode.

Outcome: He turned rough workout-logging friction into a working multi-device app workflow while keeping the AI coding process bounded by requirements, review, commits, and refactoring.

Prototyping & DesignCoding & Engineering

Use AI to close the gap between design and code by bringing production context into design work and turning design intent into more implementation-ready code changes.

Outcome: Shows how product teams can reduce handoff loss between designers and engineers. Instead of treating Figma and the codebase as separate worlds, the workflow helps design reflect what is actually in production and helps code changes preserve the intent of the design.

Business OperationsPrototyping & Design+1

Uses a multi-tool AI product management workflow to take a product idea from user research to PRD, clickable prototype, and stakeholder-ready vision video in about 20 minutes.

Outcome: Turns early product discovery into a fast, tangible package: evidence from user discussions, a structured PRD, a working prototype, and a short concept video that stakeholders can react to.

Prototyping & DesignResearch & Analysis

Uses example conversations as the starting point for AI product design, then turns those conversations into clearer requirements, product flows, and interactive prototypes.

Outcome: Shows how teams can design AI products around real conversational behavior instead of jumping straight to UI screens. The workflow helps clarify what users ask for, how the assistant should respond, where trust breaks down, and what the product experience should feel like.

Business OperationsPrototyping & Design

Uses AI to connect product management artifacts: turning PRDs into clearer Jira tickets, implementation-ready tasks, and prototypes that help teams move from product thinking to execution faster.

Outcome: Shows how PMs can reduce the handoff gap between strategy, requirements, design, and engineering by using AI to translate a product idea into more structured artifacts for the team to review and build from.

Prototyping & DesignCoding & Engineering

Uses AI coding tools to help design teams move faster from Figma to working prototypes, while creating custom skills and context that make development workflows easier to repeat.

Outcome: Shows how designers can use AI to make prototypes more real without giving up design judgment. The workflow helps teams turn visual ideas into interactive artifacts, test them sooner, and preserve the context needed for engineering follow-through.

Coding & EngineeringPrototyping & Design

Uses a dual-agent app-building workflow where one AI agent acts as the builder and another acts as the reviewer, helping a non-traditional developer move faster while keeping a second layer of critique on the work.

Outcome: Shows how AI-assisted building can become more reliable when the work is split into roles: one agent creates the first version, another inspects it, and the human decides what to accept.

Prototyping & Design

Built ProtoDash, an AI-powered internal prototyping studio that brings Stripe’s design system into AI-generated prototypes, helping designers and PMs create high-fidelity clickable prototypes instead of generic AI output.

Outcome: Turns AI prototyping from generic “blurple slop” into on-brand, shareable Stripe prototypes; supports live demos instead of static memos, helps PMs prototype, and streamlines design feedback and engineering handoff.

Prototyping & Design

Uses Cursor as a creative partner for design exploration: generating visual styles, building interactive prototypes like a working piano with sound, and refining rough AI-generated interfaces into more polished designs.

Outcome: Gives designers a faster way to explore aesthetics, prototype real interactions, and practice giving better feedback to AI. Instead of stopping at static mockups, Elizabeth uses code-based tools to create things people can click, hear, test, and iterate on.

Prototyping & DesignMarketing & Content

Uses mood boards, Midjourney style references, personalization codes, Nano Banana, Flora, and Figma to create consistent AI-generated brand imagery that can be packaged into reusable client-ready visual systems.

Outcome: Turns AI image generation from a one-off prompt experiment into a repeatable creative direction process. Clients can get not just finished assets, but a reusable visual system with references, prompts, and style settings they can keep using.

Prototyping & Design

Uses structured JSON data and a clear prompt framework to make AI prototypes and Midjourney images more realistic, flexible, and production-useful.

Outcome: Moves AI prototyping beyond vague “make it look nice” prompts. By separating data, UX, and visual style, Ravi gets prototypes that feel more like real products and images that look more curated than generic AI output.

Coding & EngineeringPrototyping & Design

Uses HTML as a richer collaboration layer with Claude Code, turning AI-generated plans, brainstorms, editing interfaces, and design systems into visual artifacts that are easier for humans to read, adjust, and share.

Outcome: Makes AI-assisted development more visible and easier to steer. Instead of reviewing giant Markdown plans, humans can inspect interactive webpages, edit disposable micro-apps, and reuse living design-system files as context for future work.