COURSE OUTLINE

Day 1: The Gemini Revolution & Vertex AI Foundations

Focus: Understanding the Google AI stack and the power of Multimodality.

• The Google AI Landscape: Evolution from PaLM to Gemini; understanding Gemini 1.5 Pro, Flash, and the "Flash-Lite" models.
• Core Concepts: Tokens, multimodal embeddings, and the "Long Context" advantage (processing 1hr+ of video or massive codebases).
• Vertex AI vs. AI Studio: Choosing the right environment for prototyping vs. production.
• Model Garden: Exploring Google-first and Open Models (Llama 3, Gemma 2) on Google Cloud.
• Hands-on Lab: Setting up a Google Cloud Project and running your first "Hello World" multimodal prompt (Text + Image + Video) in Vertex AI Studio.

Day 2: Advanced Prompting & the Gemini API

Focus: Mastering the SDK and sophisticated interaction patterns.

• Gemini Prompting Patterns: Zero-shot, Few-shot, and System Instructions tailored for the Gemini 3.0 reasoning engine.
• Structured Outputs: Forcing Gemini to return valid JSON for application consumption.
• Context Caching: Optimizing costs and latency for large-scale datasets using Vertex AI Context Caching.
• The Python SDK: Initializing the Vertex AI SDK, managing safety settings, and handling streaming responses.
• Hands-on Lab: Building a "Code Auditor" that uses the 1M+ token window to analyze an entire GitHub repository for security vulnerabilities.

Day 3: Function Calling & RAG (Retrieval-Augmented Generation)

Focus: Connecting Gemini to real-time data and external tools.

• Function Calling: Teaching Gemini to interact with external APIs (e.g., Google Search, internal databases, or weather services).
• Grounding in Vertex AI: Using "Grounding with Google Search" to provide up-to-the-minute factual accuracy.
• Vector Infrastructure: Introduction to Vertex AI Vector Search (ScaNN) for high-performance semantic retrieval.
• BigQuery for AI: Using SQL to generate embeddings and perform vector searches directly within the data warehouse.
• Hands-on Lab: Building a RAG-powered technical support bot that retrieves answers from a private library of PDF manuals stored in Google Cloud Storage.

Day 4: Agentic Workflows & Responsible AI

Focus: Moving from chatbots to autonomous agents.

• The GEAR Framework: Introduction to the Gemini Enterprise Agent Ready (GEAR) program.
• Building Agents: Using the Agent Development Kit (ADK) to create "reason-and-act" (ReAct) loops.
• Security & Governance: Managing Vertex AI IAM permissions, Data Residency, and VPC Service Controls for AI.
• Evaluation: Using Vertex AI Model Evaluation to benchmark prompt performance against ground-truth datasets.
• Capstone Project: Finalizing a "Smart Project Manager" agent that can read meeting transcripts, query a database for task status, and update a Jira-like dashboard.