This foundational module takes you from zero to making your first successful Gemini API call. You'll start by understanding why AI-powered text generation is transforming applications across industries, then dive into the essential concepts every developer needs to know: tokens, context windows, rate limits, and authentication. Through hands-on coding sessions, you'll set up your development environment, secure your API keys, and write your first text generation requests using either Python or JavaScript. By the end of this module, you'll have built a working text generation application and designed three unique use cases for different domains. This module establishes the foundation for all subsequent API work and gives you the confidence to integrate AI text generation into your projects.

This module teaches you how to make informed decisions about AI model selection and gain transparency into how your AI thinks. You'll learn the key differences between Gemini Pro and Flash models, understanding when to prioritize quality versus speed and cost. The module then introduces Gemini's unique "thinking" feature, which exposes the model's internal reasoning process. You'll learn to enable thinking, configure thinking budgets, and interpret thought summaries to debug issues and improve your AI's performance. Through practical exercises, you'll build systems that intelligently choose the right model for each task and leverage thinking insights to create more reliable AI applications. This module transforms you from making basic API calls to making strategic, informed decisions about AI behavior.

This module unlocks Gemini's powerful built-in capabilities and teaches you to format AI responses for reliable downstream processing. You'll master two essential built-in tools: URL Context for analyzing web content and Google Search for accessing real-time information. The module then focuses on structured output using JSON Schema, showing you how to transform messy, unpredictable AI responses into clean, consistent data structures. You'll discover why this combination is crucial for building AI agents—tools provide dynamic information while structured output ensures that information is actionable and parseable by other systems. Through hands-on projects, you'll build information processing systems that combine web data, search results, and structured formatting to create agent-ready outputs.