Home Blog

Generative Rapid Prototyping (GRiP)

Prototypes save time and money. You can test and present ideas, iterate, and provide specifications to developers. Traditional tools like Figma are great at this. But AI has brought about a new approach that I'm finding to have unique advantages.

I think it's useful to name things, so I'm calling it Generative Rapid Prototyping (GRiP) (as in "get a grip" on what you're going to build). The goal is to leverage AI to create multiple testable, interactive prototypes in hours instead of days.

What Is GRiP?

Traditional prototyping tools excel at visual design but fall short when it comes to testing real user interactions. Static mockups can't reveal how users actually interact with your application, and high-fidelity prototypes often require significant investment before gathering meaningful feedback.

Generative Rapid Prototyping takes a different approach: use AI-powered code generation to quickly create functional prototypes that users can actually test. These prototypes aren't just clickable mockups – they're working web applications with real interactivity and local data persistence.

Designers and developers have been discovering this approach for awhile now, and I wanted to lay out a set of principles that I've found to be the useful in designing prompts and getting the best results from AI-generated prototypes:

The 9 Principles of GRiP

1. Generative Speed

Principle: Prototypes should take hours, not days to create.

Modern AI tools can generate functional UI components, forms, and interactions very quickly. Combined with lightweight styling frameworks, testable prototypes can be created in a fraction of the traditional time. This means more time for iteration and less time wrestling with tools.

Focus on setting up an environment and prompts that encourage rapid iteration. Your goal is to figure out the problems you need to solve and how to solve them. Generative AI isn't going to get you pixel-perfect design. Pixel-perfect designs can come after.

2. Testable Interactivity

Principle: Simulate real-world user flows with basic interactivity.

Instead of simulating interactions through linked screens, prototypes handle real user actions. Users can add items to a cart, edit their profile, or filter search results – just like they would in the final product. This level of interactivity reveals usability insights that static mockups might miss.

3. Realistic Data Generation

Principle: Populate prototypes with believable, diverse data.

AI excels at generating realistic test data that reflects your user base. Populate your prototype with:

4. Local Data Persistence

Principle: Maintain prototype state across sessions.

When users interact with your prototype, their actions should persist between page refreshes and browser sessions. This creates a more realistic testing environment and allows for longer-term usability studies. Users can return to their saved state, just like in a real application.

5. Edge Case Simulation

Principle: Test realistic scenarios, including errors and outliers.

Good design accounts for everything that could go wrong. Prototypes should handle:

6. Accessibility by Default

Principle: Ensure prototypes are inclusive and usable for everyone.

Accessibility shouldn't be an afterthought. Prototypes should:

7. Rapid Feedback Loops

Principle: Build prototypes that allow for fast iterations.

Prototypes can be modified extremely quickly in response to user feedback. This means:

Tip: AI sometimes is bad at changing only one aspect of an app without inadvertently changing other things. Keep each iteration of your prototype in a separate branch of a source control repository, like GitHub, so you can revert if the AI goes a bit rogue.

8. Match Technologies

Principle: Use the same tech stack as your production environment.

By prototyping with your target technologies, you can:

9. Deployment-Ready Prototypes

Principle: Prototypes should be easily shareable and testable.

Make your prototypes accessible to stakeholders and testers:

Getting Started with GRiP

To begin implementing GRiP in your workflow:

  1. Choose your tools:

    • An AI coding assistant
    • A lightweight CSS framework or vanilla CSS
    • A modern JavaScript framework (AI tends to be good at them because it has lots of training data)
    • Basic data storage solution (localStorage or indexedDB)
    • A storage and deployment solution (where you can separately store each iteration)

  2. Create a starter template for the AI to work from:

    • Basic navigation structure
    • Brand colors and fonts in a base CSS file
    • Common UI components (optional)

  3. Use AI to accelerate:

    • Component creation
    • Test data generation
    • Interaction patterns
    • User research

Once you have a well-working prototype that both dev and design feel good about, you likely still will need to work on getting the design just right. This approach is about accelerating prototyping and working out what you should build.

Prompt Guidance

When prompting an AI to build your prototype be sure to specify:

The Future of Prototyping

GRiP represents a shift from static, visual-only prototypes to functional, testable applications.

By embracing GRiP, teams can:

Conclusion

Generative Rapid Prototyping isn't just some new tool – it's a new prototyping approach. By leveraging AI and modern web technologies, we can create better prototypes faster, leading to better products and happier users.

What should be your first step? Start by converting your next prototype from static mockups to a functional web application with local data storage. Choose a tool for generating the web application, whether it's bolt.new, Cursor, or another.

Set up easy ways to deploy. For example you can set up a GitHub repo and deploy quickly to StackBlitz.

The time you invest in setting up your GRiP workflow will pay dividends in faster iterations and better user feedback.