Validating AI prototypes: From idea to user insight

AI prototyping tools—like Figma Make, Lovable, and Bolt—can turn a prompt into a clickable design in minutes. It’s no surprise that nearly two-thirds of Product professionals already use AI coding tools in their workflows.

Speed, however, means little without validation. In a world where anything can be built quickly, the best teams know that building the right product fast is what sets them apart. Especially when new features, functionality, and feasibility need to be assessed before they ever reach the backend or introduce bottlenecks for engineers.

That’s why AI prototypes still need validation, usability testing, behavioral analysis, and real-user feedback, to ensure what looks good also works well.

AI tools make it faster than ever to go from an idea to a working prototype. Designers can input a couple of prompts and instantly get screens, layouts, and even user flows. It’s a cheat code for prototyping, but fast results don’t always mean good results, especially when teams use these early-stage flows to inform product design, rapid prototyping, frameworks, or roadmap planning.

Here are some of the reasons you need to validate these AI-generated prototypes:

These gaps lead to experiences that confuse users, slow down task completion, and lower confidence in the product. Without validation, teams risk moving full steam ahead in the wrong direction.

Here’s a look at the most effective ways to validate AI-generated prototypes and when each one is the right tool for the job.

Maze is an end-to-end user research platform that helps teams test ideas, prototypes, and live websites with real users. It brings quantitative and qualitative methods together in one place, so product teams can validate designs before development.

Testing an AI-generated prototype with Maze only takes a few steps.

At a high level, you’ll publish your AI prototype, connect it to Maze, and add the tracking code that powers real-user testing.

Each step brings your prototype closer to live validation—so you can see how people interact with your design, collect usability data, and make confident product decisions before you move to development.

Here’s what the full workflow looks like at a glance:

Here’s how to run a full validation workflow for your AI prototype in Maze, from setup to analysis.

AI prototyping tools make that first version—your MVP—faster to produce than ever. With just a few prompts, tools like Figma Make, Lovable, and Bolt can turn an idea or user flow into a clickable prototype in minutes.

Here’s an example of a website created with Figma Make:

At this stage, don’t worry about perfection. The goal is to get a tangible version of your concept that captures the main user journey, enough to test core journeys and navigation flows.

Once your AI-generated prototype looks and behaves like a minimal working product, you’re ready to bring it into Maze for validation.

Once your AI-generated prototype is ready, it needs to be live before you can test it. Whether you built it in Figma Make, Lovable, or Bolt, publishing gives you a shareable URL that Maze can recognize later.

Keep that URL handy, you’ll need it when you connect your design to Maze later on.

Once your prototype is published and shareable, the next step is to bring it into Maze. This is where validation begins. Setting up your study lets you define what you’re testing, who will take part, and how you’ll measure success.

From your Maze Home screen, click ‘Create new maze.’ You can start from scratch* or open an existing project. In this view, you’ll build your study by adding blocks.

Each block represents one part of your test, such as a task, question, or follow-up survey. Every maze needs at least one block to start.

As a best practice, we recommend that you begin your study with a multiple-choice block. This helps participants ease into the session before they start completing tasks or navigating your AI prototype.

After your warm-up questions, the next key block to add is a Website Testing block. This is where you’ll connect your published prototype, whether from Figma Make, Lovable, or Bolt.

Which brings us to the next step…

With a published AI prototype and a research maze at the ready, it’s time to connect the two. This step links your live AI-generated design to your maze so that you can begin testing your prototype with real users.

Let’s take a look.

In your maze editor, click ‘Add block’ → ‘Live website testing.’ This is where participants will complete tasks on your AI prototype.

Give the block a clear, action-focused title such as “Complete the signup process” or “Navigate to pricing.” Add a short description that explains what participants should do without overexplaining.

Copy the published URL (from step 1) from your Figma Make, Lovable, or Bolt project and paste it into the ‘Website’ field provided. Then click ‘Set up.’

For testing your prototype, Maze needs a tracking snippet. This snippet allows Maze to track clicks, paths, and interactions across your AI-generated website.

Here’s how to get it and install it into Maze:

Once it’s verified, your domain appears under Connected websites, and your prototype is fully enabled for live website testing.

Next, open your prototype in Figma Make, Lovable, or Bolt, depending on where you built it.

If you’re using Figma Make:

If you’re using Bolt:

If you’re using Lovable:

Note: If you’re on Lovable’s free plan and can’t edit code, paste your snippet in the chat and ask Lovable to add it for you.

Once the snippet is added, republish or update your site so the changes go live.
Then go back to Maze and click Verify. Maze will check if the snippet is installed and confirm that your prototype is ready for testing.

Once verified, you can continue setting up your website testing block by adding the user path you want participants to follow, such as completing a checkout, signing up, or navigating to a feature page.

Once your Live Website Test block is in place, you can start expanding your study to capture a complete picture of how users interact with your product. Maze offers a range of testing options that help you move from single-task validation to complete AI product development insights.

You can combine quantitative and qualitative blocks to understand both what users do and why they do it. Quantitative data from website testing tells you where users click, how long tasks take, and where they drop off.

You can also use surveys to collect quick impressions right after each task, asking questions like “Was this step easy to complete?” or “How confident did you feel doing this?” These moments of reflection often uncover usability issues that performance data alone can’t explain.

If your AI prototype includes a mobile flow, you can also run mobile testing to see how users experience your design on smaller screens. Mobile tests reveal interaction details like tap accuracy, scroll behavior, and visual hierarchy, critical for refining mobile-first experiences.

Once your maze is built and connected, you’re ready to bring in participants. You can either test with your own users or recruit new ones directly from Maze.

If you already have an audience, customers, beta testers, or internal users, you can invite them through Maze Reach. Reach is your built-in participant management tool. It helps you create and manage your own database of testers, organize them by tags or segments, and send studies directly to the right people.

If you don’t have your own testers yet, you can hire participants from the Maze Panel. Maze partners with trusted providers, like Prolific and Respondent, to give you access to over 3 million B2B and B2C participants in 150+ countries. You can target specific audiences using 400+ advanced filters from job role and industry to location, experience level, and device type.

Before launching, double-check your maze length (under 30 minutes for unmoderated tests) and make sure all blocks work as expected. Clear tasks and short surveys lead to better-quality data and more engaged testers.
Once everything looks good, click ‘Share’ → ‘Hire from Panel’ or ‘Send via Reach’ to launch your test.

Then, you’ll need to set your targeting criteria, such as audience type, filters, device type, and participant count, before placing your order. After that, Maze handles the invitations, consent, and response collection, and you get the results within hours.

Once your test is live and participants have completed it, Maze automatically generates a report. Every report includes both a high-level overview and detailed metrics for each task, block, or question you included in your study.

For live website testing, you’ll see how well your AI-generated prototype performed in real-world conditions. Maze calculates a usability score (0–100) based on success rate, time on task, and misclicks. The report then breaks this down by:

You’ll also get a visual path analysis, showing the exact routes participants took through your AI-generated prototype. This makes it easy to spot where users hesitated or took alternative paths, so you can refine layouts, flows, or copy.

If your study included qualitative blocks, like multiple-choice, open-ended, or opinion-scale questions, Maze aggregates and visualizes those too. You can identify sentiment patterns (“very easy” vs. “very difficult”), compare feedback across user groups, or highlight direct quotes to support design decisions.

From here, use Maze’s themes and filtering tools to group insights, flag usability issues, and plan your next iteration. You can export reports, share them with your team, or present directly in Maze’s presentation mode to align everyone on the next steps.

AI has made it faster than ever to generate ideas, screens, and even full product flows, but speed means nothing without validation. The difference between a polished concept and a successful product is how well it serves real people.

Maze helps teams close that gap. By combining AI-generated prototypes with real user testing, Maze turns quick ideas into proven experiences. You can launch a study in minutes, collect behavioral and survey data in one place, and see exactly how users interact with your design before writing a single line of production code.

Whether you’re experimenting with tools like Figma Make, Lovable, or Bolt, Maze ensures your workflow stays anchored in evidence. Each test helps you learn faster, build smarter, and deliver products that don’t just look right, but feel right to the people using them.

After all, even the smartest AI still needs a human reality check.