AI Engineering — for testers who are ready for what comes next.
Once you can build and run a Playwright suite with confidence, the next focus is AI engineering. This track teaches how LLMs and AI agents work, how to connect them to real tools, and how to test them properly.
Topics in the AI Engineering track
LLM fundamentals
How large language models work: tokens, context windows, temperature, and why they sometimes get things wrong.
Prompt engineering for testers
Write prompts that produce reliable test cases, test data and bug reports, and learn to check the output.
AI agents
What an agent is, how it plans and uses tools, and how to test agents that act on their own.
MCP (Model Context Protocol)
Connect AI models to browsers, files and APIs, including Playwright MCP for AI-driven browser automation.
RAG (retrieval-augmented generation)
Ground answers in your own documents, and measure retrieval quality and answer accuracy.
Testing LLM and AI systems
Evaluate accuracy, hallucinations, bias and safety, with repeatable checks you can run in CI.
Building AI-powered testing tools
Build small tools of your own: test-case generators, failure triage helpers and self-healing locator experiments.
AI in the QA workflow
Where AI fits from requirements to release, and where a human tester still makes the call.
Playwright first, then AI engineering.
Playwright gives you the automation foundation: locators, fixtures, the Page Object Model and CI. AI engineering builds on it, so you can use AI to speed up your testing and test the AI products companies are now shipping.