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Kulbhushan Borse
Kulbhushan Borse

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Building & Deploying Real-World AI Applications with Google AI Studio 🚀

#ai #learngoogleaistudio #devops #webdev

Generative AI is moving fast—but the real challenge is not learning models, it’s building production-ready applications around them.

After completing the “Building & Deploying Applications with Google AI Studio” program, I decided to go one step further and build a complete AI-powered web application—from prompt design to UI, persistence, and API integration.

This post walks through:

  1. What Google AI Studio is

  2. How to set it up properly

  3. Real use cases

API & GCP integrations

And how I built an AI Masterclass web app using it

Why Google AI Studio?

Google AI Studio is a developer-focused platform to prototype, test, and deploy applications using Gemini models.

What stood out to me:

  • Extremely fast prompt iteration
  • Clean API experience
  • Strong alignment with GCP services
  • Designed for builders, not just demos
  • It bridges the gap between prompt engineering and production deployment.

Google AI Studio Setup (Step-by-Step)
1️⃣ Create a Project

  • Go to aistudio.google.com
  • Sign in with a Google account
  • Create or select a GCP project

2️⃣ Choose a Model

  • Google AI Studio gives access to:
  • Gemini Pro – reasoning, chat, structured outputs
  • Gemini Pro Vision – text + image understanding

You can:

Test prompts interactively

Adjust system instructions

Control temperature, max tokens, safety settings

3️⃣ Generate an API Key

  • Create an API key directly from AI Studio
  • Restrict it to your project for security
  • Use it in frontend/backend apps
  • Prompt Engineering in Practice

Instead of “chatting”, AI Studio encourages intentional prompt design.

Example:
System:
You are an AI instructor guiding users through structured AI learning paths.

User:
Explain transformers with a real-world analogy.

Key learnings:

System prompts matter more than user prompts

Structured outputs reduce hallucinations

Prompt versioning is essential for iteration

Core Use Cases I Explored

🔹 1. AI-Powered Learning Platform

I built an AI Masterclass web app where:

Users progress through learning stages

AI explains concepts dynamically

Certification state is tracked locally

This mimics real ed-tech platforms using LLMs responsibly.

🔹 2. Prompt Evaluation & Optimization

I also explored prompt evaluation workflows, inspired by my freelancing experience:

Compare responses across prompt variants

Measure clarity, relevance, and structure

Iterate prompts systematically

This is critical in enterprise LLM deployments.

🔹 3. Presentation & Content Generation

Using AI Studio, I implemented:

Slide-style explanations

Step-by-step curriculum narration

Consistent tone across content

Google AI Studio provides clean REST APIs.

Example (JavaScript):
const response = await fetch(
  "https://generativelanguage.googleapis.com/v1/models/gemini-pro:generateContent?key=API_KEY",
  {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({
      contents: [{ parts: [{ text: prompt }] }]
    })
  }
);
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What I liked:

  • Minimal boilerplate
  • Predictable responses
  • Easy frontend integration
  • GCP Integration Possibilities
  • This is where Google AI Studio shines.

🔹 Common GCP Integrations

Cloud Functions / Cloud Run – backend inference

Firebase – auth & user state

Cloud Storage – store prompts, logs, outputs

BigQuery – analytics on AI usage

IAM – access control for enterprise apps

🔹 Typical Architecture
Frontend (React)
↓
Backend (Cloud Run / API)
↓
Google AI Studio (Gemini)
↓
Storage / Analytics (GCP)

This setup is production-grade and scalable.

My Project: AI Masterclass Web App

To apply everything, I built a full web application:

  • React + TypeScript (Vite)
  • AI-driven curriculum
  • Presentation mode
  • Local persistence (simulated backend)
  • Certification generation
  • Clean UI & branding

_
Final Thoughts
_
Google AI Studio lowers the barrier to building real AI applications, but still gives you the control needed for serious systems.

🔗 Links

GitHub Project: https://github.com/coolbhu/AI_learning_tool

Certification: Building & Deploying Applications with Google AI Studio

Platform: Google AI Studio

__by Kulbhushan

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