The 10-Minute AI Prototype

The Problem: "Vibe Coding" with AI is fast until your UI components need real data. When you ask an AI to build a paginated table or a complex chart, it either hardcodes static arrays in the component—which fails to test loading states—or assumes a backend exists that doesn't.

The Mock-First Solution: Ask the AI to build the UI component and the Mockzilla endpoint simultaneously.

1. The Prompt: "Build the UI and the Mock"

Instead of just asking for a React component, use an AI agent (like Claude Opus, GPT, or Gemini with MCP enabled) to orchestrate both the frontend and the mock server.

Your Prompt:

"I need a paginated Product List in React.

1. Use Mockzilla to create GET /api/products. Return 10 random products (id, name, price, status) and total_pages: 5. Add a 1-second delay.
2. Then, build ProductList.tsx using Tailwind. It must fetch from the mock, handle loading/error states, and support page navigation."

2. What Happens Next

The AI orchestrator will perform two distinct actions in parallel:

Action A: The Mockzilla Configuration (via MCP)

The agent calls Mockzilla's tools to create the products mock. It configures the schema so that every time the React app hits /api/products?page=2, Mockzilla generates 10 new random products instantly.

Action B: The Frontend Code

The agent writes a production-ready ProductList.tsx:

import { useEffect, useState } from 'react';

interface Product {
  id: string;
  name: string;
  price: number;
  status: 'In Stock' | 'Out of Stock';
}

export default function ProductList() {
  const [data, setData] = useState<{ items: Product[], total: number } | null>(null);
  const [loading, setLoading] = useState(true);
  const [page, setPage] = useState(1);

  useEffect(() => {
    setLoading(true);
    fetch(`http://localhost:36666/api/mock/my-project/api/products?page=${page}`)
      .then(res => res.json())
      .then(setData)
      .finally(() => setLoading(false));
  }, [page]);

  if (loading) return <div className="animate-pulse p-4">Loading Products...</div>;

  return (
    <div className="p-4 bg-white rounded-xl shadow">
      <table className="w-full">
        <thead>
          <tr className="border-b text-left">
            <th className="py-2">Name</th>
            <th>Price</th>
            <th>Status</th>
          </tr>
        </thead>
        <tbody>
          {data?.items.map(p => (
            <tr key={p.id} className="border-b last:border-0">
              <td className="py-2">{p.name}</td>
              <td>${p.price.toFixed(2)}</td>
              <td>{p.status}</td>
            </tr>
          ))}
        </tbody>
      </table>
      <div className="mt-4 flex gap-2">
        <button onClick={() => setPage(p => Math.max(1, p - 1))}>Previous</button>
        <span className="py-1 px-3 bg-gray-100 rounded">Page {page} of {data?.total}</span>
        <button onClick={() => setPage(p => p + 1)}>Next</button>
      </div>
    </div>
  );
}

3. The Result: A Living Prototype

In less than 10 minutes, you have a fully functional UI that feels connected to a production database. You didn't write any SQL, you didn't configure an ORM, and you didn't waste tokens asking the AI to rewrite a backend.

You validated the product. Now you can show it to stakeholders, get approval, and then build the real backend to match the Mockzilla contract.