Prototyping, Building, and Evaluating with AI Studios

Welcome to the next level of our Build with AI series! Now that we have covered the foundations, this session focuses on velocity and verification. We are moving into Google AI Studio to explore the high-speed "Build" mode and using the Agent Development Kit (ADK) to ensure our creations are ready for the real world.

🎯 The Objective

The goal of this workshop is to transition from simple chat prompts to production-ready applications. You will learn how to describe an app in natural language and watch as Gemini generates the full-stack code, connects a database, and provides a live preview for instant iteration.

📝 What You Will Learn

• Rapid Prototyping: Using the Build tab in Google AI Studio to generate entire application structures, including HTML, CSS, and server-side logic, through simple conversations.

• Full-Stack Capabilities: Integrating Firebase for authentication and persistent data storage automatically while you build.

• Advanced Evaluation: Moving "beyond vibes" by using the ADK’s evaluation framework to simulate user interactions and test how your agent handles complex task trajectories.

• Visual Debugging: Using the ADK Web UI to visually inspect your agent's reasoning and fix errors in real-time.

🏗️ Why This Workshop is Critical in 2026

• The Talent Race: Entering 2026, over 88% of businesses have integrated AI into their functions, creating a massive "talent vacuum" for developers who can actually ship functional tools.

• From Prototype to Production: While many can build a "demo," few can evaluate and scale an agent. This workshop teaches you the MLOps and evaluation skills needed to turn an experiment into a business-critical system.

• Increased Velocity: Modern "vibe coding" allows a single developer to act as a full-stack maker, building in hours what used to take weeks of manual coding.

👥 Who Should Join?

• Developers & Builders: Anyone who wants to turn ideas into interactive flows without getting stuck in repetitive boilerplate code.

• Data Scientists: Specifically, those looking to move their models out of notebooks and into functional web applications.

• Product Managers (PMs): PMs who want to "speak the language" of AI prototyping and rapidly test solutions with their teams.

• Tech Students & Transitioners: Learners looking to gain a competitive edge in a job market where AI roles are expected to exceed supply by 30–40% by 2027.