Welcome to our comprehensive guide on building with generative AI! Before we get our hands dirty, let's understand the two core concepts we'll be working with: Large Language Models (LLMs) and APIs. An LLM is a powerful AI model trained on a massive amount of text and code. This training allows it to understand, generate, and process human language in a remarkable way. Think of it as a super-intelligent digital brain for text. An API (Application Programming Interface) is a set of rules that allows different applications to talk to each other. In our case, the API is the bridge that lets your code send a request (like a prompt) to an LLM and receive a response (like generated text). Using an API means you don't need to host or manage the massive LLM on your own computer.

Welcome to the final section of our tutorial series! So far, you've mastered the fundamentals of the Gemini API, from your first API call to building sophisticated applications that handle stateful conversations and perform deep research. You now have a strong foundation in a wide range of Gemini's core capabilities.

This guide details advanced Gemini API features beyond basic text input/output. It covers multimodal prompts (using text, images, video, audio), function calling (connecting Gemini to external tools and APIs), building stateful chat applications, and utilizing Gemini's "Deep Research" capabilities for analyzing large datasets and generating comprehensive reports. These features enable the creation of sophisticated and interactive applications leveraging Gemini's powerful reasoning abilities.

A quickstart on making your first request to the Gemini API to generate text.