Google AI Studio Course

Lesson 1 • Your First AI Studio Interaction

Guides students through submitting their first prompt and interpreting the model response. Builds hands-on confidence before deeper feature exploration.

Lesson 2 • Understanding Generative AI Basics

Introduces foundational generative AI concepts required to use the platform effectively. Grounds technical terminology used throughout the course.

Lesson 3 • Navigating the Studio Interface

Covers the main dashboard, menus, and workspace panels. Students gain confidence moving between core interface areas efficiently.

Lesson 1 • Zero-Shot and Few-Shot Prompting

Contrasts prompting with and without examples to guide model behavior. Students learn when each approach yields better results.

Lesson 2 • Anatomy of an Effective Prompt

Breaks down the structural components that make prompts clear and actionable. Establishes a repeatable framework for prompt construction.

Lesson 3 • Chain-of-Thought Prompting

Teaches step-by-step reasoning prompts that improve model accuracy on complex tasks. Connects reasoning techniques to real problem-solving scenarios.

Lesson 4 • Prompt Templates and Reusability

Introduces parameterized prompt templates for consistent, scalable use across projects. Bridges individual prompting skills to team-level workflows.

Lesson 1 • Configuring Model Parameters

Explains temperature, top-P, top-K, and output length controls and their effects. Students gain precise control over model behavior through parameter tuning.

Lesson 2 • System Instructions and Personas

Teaches how to set persistent model behavior using system-level instructions. Students learn to shape model tone, role, and constraints for specific applications.

Lesson 3 • Gemini Model Family Overview

Surveys available Gemini model variants and their capability profiles. Provides the selection criteria needed for informed model choice.

Lesson 4 • Model Safety and Content Filters

Explains built-in safety settings and how to configure harm thresholds appropriately. Ensures students build applications that meet responsible AI standards.

Lesson 1 • Using AI Studio Chat Mode

Explores the multi-turn chat interface for building conversational workflows. Students learn to manage conversation history and context effectively.

Lesson 2 • Structured Output Formatting

Covers techniques for instructing models to return JSON, tables, and other structured formats. Enables downstream processing and integration with other systems.

Lesson 3 • Automating Repetitive AI Tasks

Teaches batch processing strategies and workflow automation patterns within AI Studio. Reduces manual effort for high-volume, repetitive generation tasks.

Lesson 1 • Gemini API Fundamentals

Introduces the Gemini API structure, authentication, and request lifecycle. Provides the technical foundation for all programmatic integrations.

Lesson 2 • Securing and Managing API Usage

Addresses API key security, usage monitoring, and cost control strategies. Ensures students build integrations that are safe and financially sustainable.

Lesson 3 • Exporting Code from AI Studio

Covers the built-in code export feature for Python, JavaScript, and other languages. Students move seamlessly from prototype to production-ready code.

Lesson 4 • Making API Calls Programmatically

Walks through constructing and sending API requests from a development environment. Builds practical coding skills for real-world AI integration.

Lesson 1 • Deploying and Managing Tuned Models

Covers how to access, version, and retire tuned models within AI Studio. Connects tuning outcomes to production deployment workflows.

Lesson 2 • Running a Tuning Job in AI Studio

Walks through the end-to-end tuning job configuration and submission process. Students gain hands-on experience launching and monitoring a real tuning run.

Lesson 3 • Evaluating Tuned Model Performance

Teaches systematic evaluation of fine-tuned models against baseline and target metrics. Ensures students can validate whether tuning achieved its intended goals.

Lesson 4 • When and Why to Fine-Tune

Clarifies the conditions under which fine-tuning outperforms prompt engineering alone. Sets realistic expectations for customization outcomes.

Lesson 1 • Building a Conversational AI Agent

Guides students through assembling a multi-turn conversational agent with memory and tools. Integrates chat mode, system instructions, and function calling into one project.

Lesson 2 • Prototyping and User Testing

Covers rapid prototyping methods and structured user feedback collection for AI apps. Students validate their applications against real user needs before final delivery.

Lesson 3 • Application Architecture Planning

Introduces design patterns for AI-integrated applications and component mapping. Students learn to plan before building, reducing costly rework.

Lesson 4 • Grounding Responses with External Data

Covers techniques for connecting model outputs to real-world, up-to-date information sources. Reduces hallucination and increases factual reliability in applications.

Lesson 1 • Cost and Latency Optimization

Covers strategies for reducing token usage, API costs, and response latency. Ensures production deployments are efficient and financially viable.

Lesson 2 • Monitoring and Maintaining Live Systems

Covers ongoing monitoring, alerting, and model refresh strategies for deployed AI systems. Ensures long-term reliability and performance after launch.

Lesson 3 • Systematic Prompt Optimization

Applies structured experimentation to maximize prompt performance at scale. Moves students from intuitive tweaking to data-driven prompt engineering.

Lesson 4 • Defining AI Output Quality Metrics

Establishes quantitative and qualitative frameworks for measuring model output quality. Gives students objective criteria to guide optimization decisions.