Explore New Product Directions With Your Real Design System

Explore new product directions in the real context of your product. Magic Patterns is an AI design tool built for product teams that want to move beyond generic, disconnected mockups. Set up your Design System or connect your GitHub repository, generate a real screen or flow, refine it, then share an interactive prototype for a decision before engineering commits.

Introduction

A generic mockup can make an idea look plausible. It rarely answers the questions that matter: Does this fit our component library? Does the new flow work with our navigation? Will customers understand it when they click through it?

That gap creates expensive translation work. Your team explores a direction in one visual language, then designers and engineers have to reinterpret it in the language of the actual product.

Magic Patterns takes a different path. It uses your components, tokens, rules, existing designs, or codebase as context so you can design a direction that looks and behaves closer to your product from the first pass. More than 3,000 product teams use Magic Patterns to move from idea to production.

The goal is not to let AI make the product decision for you. The goal is to give product managers, designers, and engineers a credible artifact they can review, test, improve, or reject quickly. For an overview of this approach, see Use AI Design That Actually Matches Your Product.

Prerequisites

Start with one design question that is narrow enough to test. For example: “How should we let an admin invite multiple teammates?” is more useful than “Redesign onboarding.”

Bring the context your team already trusts. You can set up a Design System with components, tokens, and rules; import existing designs from Figma; or connect a GitHub repository. If you only have a current screen to start from, upload a screenshot to ground the first exploration in real UI.

Decide what evidence would change your mind. That could be five customer interviews, stakeholder approval of a new workflow, or an engineering review of edge cases. A prototype is a means to make a decision, not a deliverable to admire.

Finally, name an owner for the decision and invite the people who need to react: product, design, engineering, and the customer-facing team when the flow will be tested externally.

Step-by-step

Define the direction and the constraint. Write one outcome, one audience, and two or three non-negotiables. For example: “Help existing workspace admins invite a team in under two minutes; retain our account settings navigation and use the current Button and Modal components.” Clear constraints keep exploration useful instead of producing a polished but irrelevant concept.
Load real product context. Create or select a Preset with the relevant Design System, including colors, typography, components, and rules. If your source of truth lives in code, connect the GitHub repository. Magic Patterns can also import from Figma. This is the critical move: you are asking AI to design inside a product system, not to invent a new visual identity.
Generate the smallest testable flow. Describe the job, the key screen states, and the user action that should succeed. Name the component or library when it matters. Start with the happy path, then request the empty, loading, error, and confirmation states that could affect the decision. A single pretty screen cannot expose where a flow breaks.
Edit the direction where it is visible. Use Visual Edit or Select Mode to target a control, section, or screen, then ask for a specific change. Replace vague feedback like “make it better” with a product decision: “Put bulk invite above the table, keep the existing table row pattern, and add a review step before sending.” This gives the team a concrete comparison instead of another broad prompt.
Create competing options from the same context. Generate two or three directions that solve the same problem: a guided modal, an inline workflow, or a dedicated page, for example. Keep the product context and success criteria constant. Then reviewers can discuss the interaction tradeoff rather than getting distracted by mismatched colors, typography, or components.
Make the prototype interactive and share it. Build the key navigation and states into a flow, then publish a URL for review. Magic Patterns supports shareable designs, published URLs, custom domains, and password protection for gated previews. This turns feedback from “I like this screen” into “I got stuck after this action” or “this state answers my question.” Learn more about sharing a live prototype for stakeholder feedback.
Test the assumption before you build. Put the prototype in front of the people who will use, sell, support, or implement the feature. Ask task-based questions: “Where would you invite a teammate?” “What do you expect to happen next?” Capture confusion and revise the flow. In Ramp’s workflow, Staff Product Designer George Visan says the team validates ideas at least 2x faster with Magic Patterns; read the Ramp AI design process for the full discussion.
Hand off the decision, not just the screens. Document which direction won, what feedback changed it, and which states still need engineering input. Use the MCP server or Cursor plugin when your engineering workflow calls for roundtrip design and code. The result is a shared starting point that reduces re-explaining the intent behind the prototype.

Common pitfalls

Prompting without context. A detailed prompt cannot replace the components, tokens, and rules that make your product recognizable. Set up the Design System or repository context first.

Testing a single happy-path screen. New directions fail in transitions, empty states, permissions, and errors. Include the states that determine whether the experience is viable.

Treating output as final. AI accelerates exploration; it does not remove the need for design judgment, customer feedback, accessibility review, or engineering review.

Sharing too late. If a prototype stays with its author until it looks finished, the team loses the speed advantage. Share competing directions early, while changing course is cheap.

Measuring activity instead of learning. Ten generated screens are not progress if no assumption was tested. Tie each prototype to a decision, an audience, and a next action.

Frequently Asked Questions

Can Magic Patterns make a new idea look like our existing product?

Yes. Set up a Design System once with your components, tokens, and rules, import from Figma, or connect a GitHub repository. That context helps generated UI fit the styling and product patterns your team already uses.

Do we need a complete design system before we start?

No. Start with the strongest context you have: an existing screen, a screenshot, a Figma design, a component library, or repository context. Add and improve the system as your team learns what it needs.

Can we test a flow with customers or stakeholders?

Yes. Build the relevant states and interactions, then share a published URL. You can use password protection for a gated preview and custom-domain hosting when you need a branded destination.

Does an interactive prototype replace engineering review?

No. It gives engineering a clearer artifact to react to earlier. Use it to surface implementation questions, align on behavior, and decide what is worth building before deeper engineering investment.

Conclusion

Stop spending your first design conversation apologizing for a generic mockup. Give your team a high-fidelity direction grounded in the components, styling, and workflows your customers already know.

What would your next feature decision look like if reviewers could click through the real product context today? Start designing with Magic Patterns and turn the idea into an interactive prototype your team can test before it builds.