From Prompt to Production: How I Built "Google Stadium" for Google PromptWars 2026

R.Shanmugaraj — Sun, 19 Apr 2026 10:11:38 +0000

Have you ever missed the winning goal of a match because you were stuck in a 30-minute line for a hot dog?

Managing crowds, vendor logistics, and fan experiences inside a massive stadium is a logistical nightmare. For the Google PromptWars: Virtual 2026 hackathon, I set out to solve this problem.

The result is Google Stadium: a real-time, full-stack application that provides seat-direct food delivery, live crowd traffic monitoring, and global stadium communication.

But I didn't build it alone. I built the entire architecture using Google Antigravity and advanced prompt engineering. Here is a look under the hood at how I took this idea from a blank prompt to a live, production-ready cloud application.

🏗️ ** The Tech Stack**
Before diving into the AI process, here is the architecture I decided on to handle real-time stadium data:

-Backend: Python & FastAPI (for high-speed asynchronous processing).

-Real-time Comms: WebSockets (for live global chat and order tracking).

-Database: PostgreSQL managed via SQLAlchemy & Asyncpg.

-Frontend: ReactJS built with Vite, styled with Tailwind CSS.

-Hosting: Render (Decoupled Static Site and Web Service).

🧠 The Secret Weapon: Agile Prompt Engineering
The biggest mistake developers make with AI coding agents is treating them like a vending machine—asking for an entire app in one massive prompt. It almost always results in bloated, broken code.

Instead, I used an Agile Prompting Methodology with Google Antigravity. I treated the AI like a Senior Pair Programmer, breaking the build down into strict, manageable sprints.

1. The Architecture Phase
I didn't let the AI write a single line of React or FastAPI routing until the database was bulletproof. My first prompts were strictly focused on schema design: designing the relationships between Users, Vendors, MenuItems, and Orders. Only once the foundation was solid did we move up the stack.

2. Iterative Component Sprints
Instead of prompting "build the frontend," I scoped prompts tightly:

"Build the Fan Dashboard. It must fetch menu items and allow the user to select a Block, Row, and Seat for delivery."

"Now, build the Vendor Dashboard. It must listen via WebSockets for incoming orders and update their status."

3. Surgical Debugging
AI is fantastic at writing code, but deploying to the cloud is where things get messy. Rather than manually hunting for bugs, I fed terminal errors directly back into Antigravity with strict context.

🐛 ** Squashing Real-World Deployment Bugs**
Building locally is easy; deploying to the cloud is hard. During deployment to Render, I hit two massive roadblocks that tested my prompt engineering skills.

Challenge 1: The Asynchronous Database Trap
My FastAPI backend was built using modern async Python. However, Render automatically provisions databases with a postgres:// URL, which defaults to an old, synchronous driver (psycopg2). The app crashed instantly on boot.

The AI Fix: I prompted Antigravity to inject a safety wrapper in my database.py that intercepts Render's environment variable and dynamically reformats it to postgresql+asyncpg://, allowing my async engine to connect flawlessly.

Challenge 2: The React SPA Routing Black Hole
When I deployed the Vite/React frontend as a Static Site, clicking links worked fine, but if a user hit "Refresh" on the /vendor page, it threw a 404 Error. Render was looking for a literal folder named "vendor" that didn't exist.

The AI Fix: I engineered a prompt to audit the deployment configuration and establish a _redirects fallback file, while simultaneously fixing hardcoded localhost WebSocket URLs to dynamically read import.meta.env.VITE_API_URL.

🚀 The Final Result
By maintaining strict prompt boundaries and utilizing iterative error correction, I successfully deployed a complex, decoupled, full-stack application.

Google Stadium is now live. Fans can order food, vendors can track revenue, and admins can broadcast live messages to the entire stadium.

Working with Google Antigravity taught me that the future of software engineering isn't just about knowing syntax; it’s about system design, understanding the tools, and knowing exactly how to ask the right questions.

🔗 ** Links**
Live Application: https://google-stadium-app.onrender.com/

GitHub Repository & Prompt Vault: [https://github.com/Shanmuga-Raj27/Google-Stadium-]

A huge thank you to Google for hosting PromptWars 2026. Happy coding!

DEV Community: R.Shanmugaraj

From Prompt to Production: How I Built "Google Stadium" for Google PromptWars 2026