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Unclaimed ProfileIn the rapidly evolving landscape of software quality assurance, the fusion of AI with traditional testing tools is a game-changer. This comprehensive course, led by industry expert Karthik KK, bridges the gap between classic test automation and modern artificial intelligence. It empowers QA engineers, SDETs, and developers to build resilient, next-generation automation frameworks from the ground up. By utilizing cutting-edge tools like Playwright and Selenium alongside Large Language Models (LLMs), learners will discover how to combat the perennial issue of flaky UI tests. The curriculum covers essential concepts such as prompt engineering, context engineering, and the practical implementation of self-healing locators. Additionally, the course dives deep into running local LLMs to ensure data privacy and reducing API costs while maintaining robust test suites. Through hands-on exercises, participants will master caching techniques, visual regression testing, and intelligent error handling. Whether you are aiming to modernize an existing test suite or design a future-proof automation framework from scratch, this course delivers the practical skills required to stay ahead in the AI-driven tech landscape.
About the creator
Karthik KK
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Karthik KK is a dedicated technical educator and software engineering professional recognized for his pragmatic approach to teaching complex AI and automation concepts. With a strong focus on bridging the gap between theoretical machine learning and practical application, Karthik has become a trusted voice for developers and testers aiming to modernize their skill sets. His instructional methodology prioritizes hands-on experience, ensuring that students do not just watch lectures, but actively build, test, and deploy functional AI-driven solutions. He specializes in integrating advanced technologies like local LLMs, Ollama, and sophisticated browser automation frameworks into real-world software testing pipelines. By focusing on…Show more
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Learning format
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Price may change · updated within 1–2 weeks
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What You'll Learn
- Master the fundamentals of prompt engineering, context engineering, and vibe coding tailored specifically for automated testing scenarios.
- Build self-healing test automation frameworks using Playwright and Selenium to dynamically repair broken locators and eliminate flaky test results.
- Deploy and run local Large Language Models (LLMs) alongside cloud-based models to perform cost-effective, secure, and offline test analysis.
- Implement advanced caching strategies and visual regression testing to optimize framework speed and visual accuracy across different browsers.
- Design a robust test architecture from scratch that incorporates AI-driven error diagnostics, automatic retries, and intelligent test execution.
- Apply practical context-engineering techniques to pass application state directly to LLMs for real-time debugging and test code generation.
Best For
- SDETs and QA engineers aiming to modernize legacy automation suites with AI capabilities.
- Developers looking to reduce test flakiness through self-healing locators and intelligent diagnostics.
- Automation testers interested in integrating Large Language Models and prompt engineering into their existing workflows.
- Tech leads responsible for architecting resilient, future-proof testing frameworks using Playwright and Selenium.
Not For
- Absolute programming beginners who have never written a script in languages like Java, C#, or TypeScript.
- Manual testers who are not interested in learning coding or advanced software engineering patterns.
- Professionals seeking a high-level theoretical overview of AI without hands-on framework implementation.
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