Overview

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By completing this course, you will learn to apply Generative AI, GitHub Copilot, and Claude Code across the entire software testing lifecycle to dramatically speed up and improve QA automation. You will master Prompt Engineering for test case generation, test data design, and plan creation. Additionally, you will build AI agents and integrate the Model Context Protocol (MCP) using Playwright, Selenium, Chrome DevTools, and AWS. You will also learn how to run private local LLMs using Ollama for secure testing. This course is designed for QA Engineers, software testers, and automation specialists who want to stay ahead of the curve by integrating AI assistants into their daily workflows. By the end of this course, you will transition from manual or traditional automated testing to AI-assisted QA, allowing you to build robust Playwright test projects, automate API testing, and design reusable prompt patterns that ensure reliable and highly maintainable test execution.

Syllabus

Course Introduction
- Get introduced to the course structure, understand the key tools used for AI-assisted quality assurance, and access the course resources.
AI powered Quality Assurance - introduction
- Explore the fundamentals of Generative AI, Large Language Models (LLMs), and their direct applications, advantages, and limitations in manual and automated software testing.
Prompt Engineering for QA: Getting the Best from AI
- Master prompt engineering principles, patterns, constraints, and context engineering techniques to optimize AI output for software quality assurance.
Checkpoint: What You've Learned So Far
- Review and consolidate your understanding of Generative AI fundamentals, prompt engineering principles, and manual testing workflows.
Hands on: AI assisted coding - GitHub Copilot and Playwright
- 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.
Hands on: AI assisted testing - Claude Code
- Install and run Claude Code to initialize Playwright projects, generate test plans, maintain testing suites, and manage persistent guidelines via CLAUDE.md files.
AI Powered API Testing for QA Engineers
- Learn to utilize AI tools to generate automated API documentation and create and debug API request collections for software testing.
AI Agents & MCP for QA Automation
- 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.
Agentic AI: Skills for QA engineers
- Explore the concept of skills within agentic AI, write your first custom skill, and implement real-world QA automation skill examples.
Playwright AI & Agents
- Automate test projects using Playwright AI, read pages without selectors via snapshots, and leverage the Playwright CLI to plan, test, and heal.
Advanced AI browser test automation
- Understand browser authentication mechanisms, utilize browser-based AI testing tools like the Claude Chrome extension, and configure authentication in Playwright AI.
Running LLMs Locally: Private AI Testing with Ollama
- Install Ollama, select and configure local LLM models, and run them privately inside Claude Code and GitHub Copilot for secure testing workflows.
CI/CD for AI-Assisted Testing
- Explore GitHub Actions fundamentals for QA and learn how to optimize workflows to run AI-assisted test automation suites in CI/CD pipelines.
Ending and next steps
- Wrap up the course, review your accomplishments, and explore next steps and optional resources for further professional development.
Final Exam

Taught by

Alex Dan

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