You know the feeling. You ask AI to build an app, and what comes back is just… off. The spacing is weird, the colors clash, and it has that unmistakable generic look. We call it AI slop. The problem isn't that AI can't build good interfaces. The problem is that we don't give it enough to work with. We toss in a one-line prompt and expect a masterpiece. That's not how it works.
I spent a lot of time figuring out a repeatable process that actually produces applications that look high-end, not like something a robot assembled in ten seconds. I'm going to walk you through exactly how to do this in five simple steps. By the end, you'll be able to build applications that look like the one I built: a financial influencer tracker that has clean stat cards, detailed performance charts, individual creator profiles, and even smooth scroll animations on the about page. It looks polished. It looks intentional. It doesn't look like slop.
Here's the path we're going to take. First, we collect our context. We brain dump everything to AI so it knows precisely what we're trying to build. Next, we find inspiration and clone an existing application. Then we take that cloned version and build our own version on top of it using the context we collected. Once the core is built, we restyle the entire application. Finally, we add scroll animations and deploy the whole thing to Cloudflare so anyone can see it.
Step 1: Collect Your Context Through a Grilling Session
Most people skip this step entirely. They give AI a vague prompt, AI guesses at the features and style, and the accuracy plummets. If you actually give enough context so AI fully understands what you're building, the accuracy goes way up. The challenge is that AI doesn't naturally know what context to collect. It won't ask the right questions unless you teach it to.
There's a skill for that. It's called the Grow Me skill, and it teaches AI to relentlessly interview you until it fully understands your project. You install it, trigger it, and then explain what you want to build. I started with something dead simple: "I want to build an investor tracker application that focuses on tracking influencers who talk about when to buy and when to sell." That's it. I didn't need a deep, detailed prompt. The skill took over from there, asking me clarification questions one at a time. What's the mock data look like? What tech stack? What's version one?
I paused and answered those questions thoroughly. That's the key. Don't rush through this part. Once the grilling session was complete, I had two files: a decisions MD file and a spec MD file. The decisions file logs every single decision we made during the conversation, so we don't lose context when we're 30 or 100 questions deep. The spec file aggregates everything into a summarized document. At this point, you've collected all the context for your application. AI now knows exactly what you're trying to build, not just guessing at it.
Step 2: Clone an Existing Application Instead of Starting from Scratch
Here's where most tutorials go wrong. They tell you to create a wireframe, design a template from a design system, browse Dribbble or Pinterest, or use some skill to generate a layout. I've tried all of those methods, and I don't find the results accurate enough. What I do instead is clone an existing working template and then build something different on top of it.
Think about the applications you use every day. There's probably one where you've thought, "If I just tweaked a few parts of this and adapted it to my niche, it would be perfect." That's exactly the approach. Clone the application first, then dump your context onto it. Change the colors, change the names, change how elements function.
For my financial influencer tracker, I looked at CoinMarketCap. They have a dashboard that tracks all the coins. I wanted something similar, but instead of tracking coins, I'd track influencers. The layout would be almost identical: a dashboard with stats, individual profiles you can click into, performance charts. Instead of coin names, I'd have influencer names. Instead of price changes, I'd track their win percentages compared to the S&P 500. Instead of market cap, I'd show how many successful calls they've made.
There are two ways to find something to clone. One is having a working website or app you already know you want to use as a base. The other is browsing templates on platforms like Vercel. Vercel has a template marketplace with fully built applications you can literally clone and build on top of. They have e-commerce stores, SaaS dashboards, all kinds of things. You can view a live demo, check out the source code on GitHub, and see that the entire codebase is right there, ready to be adapted.
The reason I recommend this approach is that as a senior software engineer who's worked at companies like Amazon and Microsoft, I've realized you don't need to be a UX design expert to build something that looks professional. You just need a solid starting point.
Step 3: Build Your Version One by Merging Context with the Clone
Once you have your cloned application and your spec file, the next step is merging them. You feed AI the cloned codebase and your context document, then tell it to build your version one. The clone gives you the structure, the layout, the proven design patterns. Your context gives you the specific features, the data model, the unique elements that make it yours.
In my case, the application started taking shape quickly. I had a dashboard with stat cards, a list of creators I could track, individual pages for each creator with performance charts, and even a ticker symbol page where you could see exactly when an influencer called a buy or a sell. All the core functionality was there because the clone provided the skeleton and my spec provided the organs.
The important thing here is that you're not just reskinning something. You're fundamentally changing what the application tracks and displays. The clone gives you a proven user experience pattern. Your context transforms it into something entirely new.
Step 4: Restyle the Entire Application
After version one is built, it's time to make it look unique. This is where you break away from the clone's visual identity. You change the typography, the color scheme, the spacing, the component styles. The goal is to make it unrecognizable from the original while keeping the solid UX foundation.
I used a tool called Impactables for this. It lets you restyle an entire application by giving it different taste parameters. You can generate multiple versions with different visual directions. For my application, I had version one with one color palette and typography set, version two shifted to the right with a different feel, and a current version that combined the best elements.
I also generated different versions of the text content. The about page, for example, explains what the application does when someone lands on it. I fed in the context we built initially and had AI choose the right words. The result was a landing page that looked professional, a dashboard that felt custom, and individual pages for creators and ticker symbols that all felt cohesive.
At this stage, the entire UI application was fully built. All the functionalities worked. The landing page was complete. The dashboard let you click into different pages. You could click into actual creators, see their performance, click into ticker symbols to see charts, add comments, mark things as bullish or bearish, sign in, sign up. Everything was there.
Step 5: Add Scroll Animations and Deploy
The final polish comes from animations. I used Hfield Cense 2.5 to add scroll animations to the about page. When a user navigates to that page and scrolls down, they see visual animations playing in the background. It's the kind of detail that separates a normal AI-built application from something that feels high-end.
Once the animations were in place, the last step was deployment. I used Cloudflare because it lets you deploy for free and doesn't require a public repository like Vercel or GitHub Pages would. You create an account, run a few commands in your terminal to authorize the Cloudflare CLI, and then let AI handle the build and deployment steps. Within minutes, I had a live URL.
I copied that URL, pasted it into a browser, and the entire application was there. Every route worked. The about page loaded with all its assets and screenshots. The dashboard was functional. The charts rendered correctly. The portfolio tracking, the call tracking, everything was live. I could share that link with anyone and they could see the full application.
Points clés à retenir
- AI guesses without context. If you don't provide detailed context, AI will guess at your features and style, and the accuracy will be low. Invest time in the grilling session.
- Use a skill to collect context properly. The Grow Me skill teaches AI to interview you relentlessly, asking one question at a time until it fully understands your project. The output is a decisions file and a spec file that capture everything.
- Clone an existing application instead of designing from scratch. Find a working app or a Vercel template that has a similar layout to what you want. Clone it, then dump your context onto it to transform it into your own application.
- Restyle to break away from the clone. Use tools to change typography, colors, and component styles. Generate multiple versions and pick the best direction. The goal is a unique visual identity on top of a proven UX foundation.
- Animations add the high-end feel. Scroll animations on pages like the about section make the application feel polished and intentional, not like generic AI output.
- Deploy for free with Cloudflare. You don't need a public repository. Cloudflare's free tier and CLI let you deploy quickly and share your application with anyone.
This entire process, from zero context to a fully deployed, polished application, is repeatable. You collect your context, clone a solid foundation, build your version one, restyle it, add animations, and deploy. The result is something that doesn't look like AI slop. It looks like an application someone actually designed.