AI-Driven Test Automation: Playwright, Selenium, LLMs & More

Transform Your Test Automation Code (Selenium & Playwright) with AI-Powered Intelligence This comprehensive course teaches you how to build intelligent, resilient test automation frameworks that leverage local LLMs to reduce maintenance, improve test stability, and accelerate bug detection—all while maintaining complete test integrity. What You'll Learn: Foundation & Setup Install and configure local LLMs using Ollama Understand prompt engineering, context engineering & model behavior Master the fundamentals of effective AI communication Understanding Automation Problems & AI Solutions Identify common automation pain points Analyze traditional automation limitations Learn how AI enables self-healing of locators in Selenium Master prompting LLMs for alternative locators Building Foundation - Local & Cloud LLM Communication Build code to access local LLMs via Ollama API Build code to access cloud LLMs via OpenAI API Create centralized configuration to access LLMs Understand how to extract LocatorType and LocatorValue from Selenium POM Understand passing PageSource and Locator Context to LLMs Building Intelligent Locator Strategy for Selenium Understand deserialization of LLM responses Implement AI Healing to get alternative locators from LLMs Build AI Healing for different locator types (XPath, CSS, ID, Name) Perform AI Healing code within self-healing logic Building Intelligent Playwright Automation Implement Self Healing for Playwright Handle Different Locator Types AI Healing in Playwright Ensuring existing Selenium Test code logic works for Playwright with POM Building Visual Testing with Vision LLM Model Understanding Vision Models and using them for Testing How to prompt Vision model to get Page comparison Implementing E2E Vision comparison By completing this course, you'll master building intelligent test automation frameworks using Selenium and Playwright enhanced with local LLMs through Ollama. You'll confidently implement AI-powered locator strategies that reduce maintenance while preserving test integrity, create self-healing mechanisms with human oversight, and use prompt engineering to generate test scenarios from natural language