Explore the fundamentals of Generative AI, Large Language Models (LLMs), and their direct applications, advantages, and limitations in manual and automated software testing.

Master prompt engineering principles, patterns, constraints, and context engineering techniques to optimize AI output for software quality assurance.

Review and consolidate your understanding of Generative AI fundamentals, prompt engineering principles, and manual testing workflows.

Set up GitHub Copilot, initialize a Playwright test project, and use AI-assisted coding to generate test plans, review test cases, and implement the Page Object Model.

Install and run Claude Code to initialize Playwright projects, generate test plans, maintain testing suites, and manage persistent guidelines via CLAUDE.md files.

Learn to utilize AI tools to generate automated API documentation and create and debug API request collections for software testing.

Discover agentic AI, explore the Model Context Protocol (MCP), and learn to install and configure MCP servers in Claude Code and GitHub Copilot to connect AI agents to real testing tools.

Explore the concept of skills within agentic AI, write your first custom skill, and implement real-world QA automation skill examples.

Automate test projects using Playwright AI, read pages without selectors via snapshots, and leverage the Playwright CLI to plan, test, and heal.

Understand browser authentication mechanisms, utilize browser-based AI testing tools like the Claude Chrome extension, and configure authentication in Playwright AI.

Install Ollama, select and configure local LLM models, and run them privately inside Claude Code and GitHub Copilot for secure testing workflows.

Explore GitHub Actions fundamentals for QA and learn how to optimize workflows to run AI-assisted test automation suites in CI/CD pipelines.

Wrap up the course, review your accomplishments, and explore next steps and optional resources for further professional development.