Every product team knows the tension between speed and clarity. Moving from a concept on a slide to something people can actually test is often slow, expensive, and fragmented. That gap between what we imagine and what we can demonstrate is where many good ideas stall.
Figma Make enters this space with a bold promise: to compress the distance between idea and proof, giving teams the power to build realistic, functional prototypes directly from their designs.
The Core Problem: Ideas Are Hard to Show
As a product designer, I live in the idea-to-pitch loop with a constant question: How do I make my teammates or clients see what I see?
Mockup help. Words help. But they’re not enough. Whether it be pitching to investors, scoping with clients, or aligning internal teams, ideas are always easy to talk about, but hard to show.
We're stuck in a gap between the concept and the tangible, interactive experience needed to get real feedback. The problem is that turning a raw idea into a functional prototype is slow, expensive, and often out of reach. Too often, we pitch promises, not proof.
For non-designers, this gap is even wider. Turning an idea into something tangible often means wrestling with complex tools, waiting on design or engineering support, or worse, going into critical meetings with nothing but words.
This is the exact gap Figma Make aims to fill. It’s a new set of capabilities designed to turn ideas into clickable, functional prototypes faster than ever.
The Solution: What Figma Make Offers
Figma Make introduces a new approach to prototyping, allowing teams to build dynamic, data-driven web applications and prototypes directly from their designs.
Here’s what I think makes it different:
Prompt-to-App: Using AI (currently Claude 3.7 Sonnet), I was surprised by how quickly I could turn a static layout into something clickable simply by providing a Figma design and a natural-language prompt.
Point-and-Prompt: Being able to select a component on the canvas and adjust its behavior with a short instruction made iteration much faster. It felt closer to having a conversation with the tool rather than switching between menus or searching for the right setting.
Reasoning & Error Detection: This is my personal favorite. It can explain its logic, manage versioning, and even offer automatic error detection and code quality improvements with the "Fix for me" feature.
Integrated Backend Support: Figma Make allows prototypes to: a) connect to real APIs, databases, and custom backend logic; b) build dynamic experiences like form submissions, authentication, or AI-driven interactions; c) integrate directly with tools like Xano, Supabase, Airtable, or other custom endpoints. I don’t need a technical background to set any of this up, yet I can still test real product flows.
Essentially, it moves prototyping from just "linking screens" to "building mini-apps."
Putting It to the Test: A Hackathon Case Study
I recently had the chance to explore Figma Make’s full potential during CodeLink’s 2025 Hackathon.
Our team decided to build an AI Tutor App that would generate a complete learning course, including lectures, practice questions, and a final project, all from a single uploaded lecture. Users could customize the project's theme and difficulty based on their learning objectives.
As the team's designer, I was responsible for building the end-to-end working prototype so we could rapidly test our ideas.
My technical background is quite limited, but the open dialogue and natural language interface of Figma Make immediately lowered the barrier. Because it’s built right within Figma, it provides a convenient and safe "sandbox" environment. I could try new ideas without any risk, thanks to the built-in versioning and revert options.
We started with a static design and a simple prompt:
Use the reference design to build an AI Tutor App that generates learning courses based on uploaded lectures. Generate Selection Questions for practices, and create a course project with a custom topic, based on user learning objectives
In a remarkably short time, we had a functional prototype that the team could actually use and test, not just click through.
To test out the resulting prototype, read our full article here.
Make the Most of Figma Make
These practices make the AI more accurate, reduce rework, and keep your prototype manageable as it grows in complexity.
Define Specific Requirements: Be explicit about what the design needs to achieve. Specify where it fits in the product and any constraints. Clear inputs lead to more accurate outputs. For example, “This screen is part of onboarding, must include email + OTP fields, and follow mobile layout rules.”
Prepare a Clean Design File: Before prompting, organize your Figma file. Remove unused layers, group related components, and standardize naming (e.g., Button/Primary, Form/Input). A tidy structure helps Figma Make understand hierarchy and reduces errors.
Break Down Complex Tasks: For multi-step flows or advanced logic, start small. Instead of prompting “build the full checkout flow,” begin with “create the cart summary section,” then layer in payment, validation, and API steps gradually. This keeps the build predictable and easier to refine.
Final Takeaways
My single biggest discovery is just how much value Figma Make adds before the official design phase even has to start. We’re not just building faster, but also learning faster:
Designers can test usability and interaction logic earlier.
Product owners can demonstrate scope and feasibility with live prototypes.
Clients and stakeholders can interact with a concept rather than interpret slides.
Teams can quickly get a general feeling for an idea, uncover real user pain points, and test complete flows in a realistic, data-driven environment. This creates a tighter feedback loop of Idea → Interaction → Insight and turns discussions into evidence-based iteration.
For product owners, designers, founders, or anyone stuck in the loop of talking about their ideas, Figma Make might finally be the tool that lets the idea truly show itself.
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