Learning Objectives

• Authenticate to the Gemini API and make requests using the Google Cloud SDK and REST
• Select between Gemini Pro and Flash models based on latency, cost, and capability requirements
• Construct multimodal prompts that combine text, images, video, and audio inputs
• Implement function calling to connect Gemini responses to external tools and APIs
• Ground model responses using Google Search to reduce hallucination and improve factual accuracy
• Generate and store text embeddings for semantic search and retrieval workflows
• Build a retrieval-augmented generation pipeline using embeddings and a vector store
• Integrate Gemini API calls into a web application using a Node.js or Python backend
• Call the Gemini API from an Android or iOS mobile client using appropriate SDKs
• Apply prompt engineering techniques including system instructions, few-shot examples, and output formatting

Course Outline

Module 1: Gemini API and Vertex AI Studio Fundamentals

This module establishes the foundational context for the course. Students are introduced to the Gemini model family, the distinction between the Gemini API accessed via AI Studio and the Vertex AI-hosted version, and where each fits in a production architecture. The module covers project setup, enabling the necessary Google Cloud APIs, and authenticating with service accounts and API keys. Students make their first API calls using the SDK and inspect the request and response structure.

Module 2: Working with Gemini Pro and Flash Models

Students examine the practical differences between Gemini Pro and Gemini Flash, including context window sizes, latency profiles, pricing, and quality trade-offs. The module covers how to select the right model variant for a given task and how to configure generation parameters such as temperature, top-p, top-k, and max output tokens. Students run side-by-side comparisons using realistic prompts to observe behavioral differences between model tiers.

Module 3: Prompt Engineering and System Instructions

This module covers structured approaches to writing effective prompts for Gemini. Topics include zero-shot, one-shot, and few-shot prompting, setting system instructions to control model persona and behavior, and using output format constraints such as JSON mode and markdown. Students practice iterating on prompts in Vertex AI Studio before moving those prompts into code, and they learn how to use the prompt gallery and saved prompt management features.

Module 4: Multimodal Input: Images, Video, and Audio

Gemini’s multimodal capabilities are explored in depth. Students learn how to pass image data inline as base64 and by referencing Cloud Storage URIs, how to submit video clips for analysis, and how to include audio inputs in prompts. The module covers practical tasks such as image captioning, document understanding from scanned images, video summarization, and combining multiple modalities in a single request. Token counting for multimodal inputs and its cost implications are also addressed.

Module 5: Function Calling and Tool Use

This module covers Gemini’s function calling feature, which allows the model to request execution of developer-defined tools rather than generating a free-text answer. Students define function schemas, handle the model’s function call responses, execute the functions in their own code, and return results back to the model for a final response. The module includes multi-turn function calling scenarios and discusses patterns for connecting Gemini to external APIs, databases, and internal business logic.

Module 6: Grounding with Google Search

Students learn to configure Google Search grounding to anchor Gemini responses in current, factual web content. The module explains how grounding differs from standard generation, how to enable and configure it in both Vertex AI Studio and the API, and how to interpret the grounding metadata and citations returned with each response. Use cases such as research assistants, news summarization, and fact-checking pipelines are used as practical examples throughout the module.

Module 7: Text Embeddings and Semantic Search

This module introduces text embeddings generated via the Vertex AI text-embedding models. Students learn how embeddings represent semantic meaning as vectors, how to generate embeddings for documents and queries, and how to measure similarity using cosine distance. The module includes practical exercises storing and querying embeddings using a vector database, and it covers chunking strategies for long documents and choosing the appropriate embedding model for a given task.

Module 8: Retrieval-Augmented Generation Pipelines

Building on the embeddings module, students construct a complete RAG pipeline that retrieves relevant document chunks at query time and injects them into a Gemini prompt as context. The module covers the end-to-end data flow from document ingestion and embedding storage through retrieval ranking and response generation. Students evaluate retrieval quality and experiment with prompt templates that make effective use of retrieved context. Common failure modes such as context overflow and irrelevant retrieval are discussed and mitigated.

Module 9: Multi-Turn Conversations and Chat History Management

Students implement conversational applications using the Gemini chat API, which maintains message history across turns. The module covers how conversation history is structured, strategies for managing context window limits in long conversations, and how to inject or prune history to maintain coherent dialogue. Students build a stateful chat interface and discuss patterns for persisting conversation state in a backend datastore between user sessions.

Module 10: Integrating Gemini into Web Applications

This module focuses on backend integration using Python (Flask or FastAPI) and Node.js. Students build a server-side API layer that accepts client requests, calls the Gemini API, and streams responses back to a browser frontend using server-sent events. Topics include handling streaming responses, error handling and retry logic, rate limit management, and securing API keys so they are never exposed to the client. A simple React or plain JavaScript frontend is used to demonstrate the full request lifecycle.

Module 11: Integrating Gemini into Mobile Applications

Students integrate Gemini into native mobile clients using the Firebase AI Logic SDK for Android and iOS. The module covers SDK setup, authenticating mobile clients through Firebase App Check, calling text and multimodal Gemini endpoints from Kotlin and Swift code, and displaying streaming responses in a mobile UI. Security considerations specific to mobile clients, such as avoiding direct API key usage in app bundles, are addressed throughout.

Module 12: Production Considerations: Safety, Monitoring, and Cost Control

The final module prepares students to move Gemini-powered features toward production readiness. Topics include configuring safety settings and harm filters, reviewing blocked response handling, and setting up Cloud Monitoring and logging for Vertex AI API calls. Students learn to estimate and control costs using token budgeting and model selection strategies. The module closes with a review of quotas, request throttling, and how to request quota increases for production workloads.