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Why a Fine-Tuned UX Model Delivers Better Results Than a Base AI

When it comes to diagnosing and improving user experience, accuracy matters. So does speed. At Flywheel Growth, we’ve built our platform around the idea that fast, affordable usability testing shouldn’t come at the cost of actionable insight.

And that’s where most AI tools fall short.

While base AI models like GPT-4o are powerful generalists, they struggle with the nuance and specificity needed to truly understand—and improve—digital product experiences. That’s why we’ve taken a different approach: we fine-tuned a proprietary model specifically trained to act as a UX research and usability expert.

Here’s why that decision matters—and why it leads to better results.

General-Purpose AI vs. UX-Specialist AI

Base models are trained on vast swaths of the internet. That makes them great at holding conversations and answering general questions—but it also means they’re easily distracted by surface-level insights. Ask a base model to analyze a landing page, and you might get generic advice like “make the CTA clearer” or “use more whitespace.”

Flywheel’s fine-tuned model, on the other hand, was trained on:

• Real-world UX review and teardown examples
• Heuristics and frameworks from leading UX authorities
• Patterns from hundreds of usability test findings and design critiques
• Structured UX research principles and behavioral science insights

As a result, our AI doesn’t just “look” at a screen. It "uses" the software and then thinks like a seasoned UX professional, identifying subtle usability issues, prioritizing what actually matters to users, and recommending fixes that align with product goals. Here’s what that means to our customers.

Speed + Precision = Actionable Insights

Most usability efforts fall into two traps:

1. They’re too slow to inform fast-moving teams
2. They produce too much noise and not enough actionable insight

By combining unmoderated testing workflows with our fine-tuned expert model, we’ve eliminated both problems. You get:

• Fast feedback loops (tests completed in days, not weeks)
• Expert-level recommendations, not just raw observations
• Clear prioritization of issues, so you know what to fix first

This is especially critical for startups and growing teams, where time and resources are limited. A generic AI might spot that your form is “a bit long.” Our AI explains why users are dropping off midway and which question to remove first to boost completion rates.

AI Trained on Real Problems, Not Hypotheticals

We didn’t train our model on abstract UX principles alone—we trained it using real customer flows, prototype reviews, and task-based usability data. That means:

• Our AI understands onboarding, checkout, conversion, and mobile-specific patterns
• It doesn’t get lost in theory—it makes suggestions grounded in actual product usage
• It can simulate persona-specific experiences, giving you feedback that mimics real users

As a result, our AI doesn’t just “look” at a screen. It thinks like a seasoned UX professional, identifying subtle usability issues, prioritizing what actually matters to users, and recommending fixes that align with product goals. Here’s why that matters.

What This Means for You

If you're building digital products and trying to improve the user experience without slowing your team down, here’s the bottom line:

• You don’t need to become a research expert
• You don’t need a big budget
• You don’t need to wait weeks for results

With Flywheel Growth, you get expert-level UX insights delivered faster, and grounded in proven principles—because our model was built to do just that.

If you’ve tried generic AI tools and been disappointed with the results, we get it. That’s why we built Flywheel Growth around the belief that specialized AI beats generalized AI when the stakes are high—like when you’re about to ship a new feature or redesign a critical flow.

Get started now →flywheelgrowth.co

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