Learn Generative AI for Software Testing

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Unlock the Power of Generative AI and Advance Your Testing Career Are you a Manual Tester, QA Engineer, or Automation Tester looking to stay ahead in today’s fast-paced testing landscape? This course is designed specifically for testers and QA professionals who want to harness the power of Generative AI to enhance productivity and expand their testing capabilities. What You’ll Learn in This Hands-On Course Gain clarity on AI vs Generative AI — understand the differences and key concepts. Explore popular Large Language Models (LLMs) such as ChatGPT, Google Gemini, and DeepSeek. Master the art of Prompt Engineering — the foundation for getting accurate and relevant outputs from AI tools. Learn to generate Test Plans, Test Cases, Test Data, and Bug Reports within seconds using Generative AI. Discover how to apply AI in Selenium Automation, API Testing, and Database Testing . Use GitHub Copilot to accelerate coding, fix bugs faster, and effortlessly generate documentation. Why This Course is Essential for Testers The future of software testing is increasingly AI-powered — from writing test scripts to generating test data, performing exploratory testing, and optimizing SQL queries. Generative AI can assist testers at every stage of the testing lifecycle. Whether you are a Manual Tester aiming to boost your efficiency or an Automation Engineer looking to streamline scripting, this course equips you with practical AI skills to future-proof your career. What You’ll Receive Step-by-step demonstrations for setting up ChatGPT, Google Gemini, and DeepSeek. Real-world examples showing AI applications in Manual Testing, Automation, API Testing, and Database Testing . Hands-on exercises and tailored prompts to practice using AI for testing tasks. Expert tips to save time, reduce errors, and improve test coverage using AI. Guidance on using GitHub Copilot to accelerate and enhance automation work. Who Should Enroll This course is ideal for: Manual Testers and QA Engineers Automation Testers (Selenium, API, Database Testers) Test Leads and Test Managers Anyone interested in understanding how AI is transforming the testing domain Prerequisites Basic knowledge of manual testing, automation testing, and SQL is recommended — no prior AI experience is required. Future-proof your QA career by mastering Generative AI tools and techniques. Enroll now and stay ahead! Course Curriculum Chapter 1: Introduction to AI and Generative AI Understanding Artificial Intelligence (AI) and real-world applications What is Generative AI ? Real-world examples Differences between AI and Generative AI Introduction to Large Language Models (LLMs) Why every QA professional should learn Generative AI Overview of popular Generative AI models (ChatGPT, Google Gemini, DeepSeek, and more) Chapter 2: Exploring Large Language Models (LLMs) What exactly is an LLM ? How Large Language Models work Step-by-step setup for ChatGPT, Google Gemini, and DeepSeek Understanding LLM features and techniques for effective interaction Chapter 3: Introduction to Prompt Engineering What is a Prompt and why does Prompt Engineering matter? Key elements of a well-formed prompt : Instruction Context Input Data Output Indicator Essential Prompt Engineering techniques: Zero-shot Prompting One-shot Prompting Few-shot Prompting Chapter 4: Quick Recap of Manual Testing Fundamentals Key manual testing concepts and terminology Chapter 5: Applying Generative AI in Manual Testing Instantly generate comprehensive Test Plans Automatically create Test Scenarios and Test Cases Generate Test Data on demand Use AI to draft Bug Reports quickly Generate Test Execution Reports with minimal effort Chapter 6: Using Generative AI in Selenium Automation Automatically generate Selenium Test Scripts Debug errors with AI-suggested solutions Auto-generate XPath and CSS Selectors Generate test data for automation runs Create documentation for test cases automatically Generate Automation Reports using AI Convert code between languages and frameworks Migrate existing automation frameworks with AI assistance Optimize XPath and locator strategies with AI Use AI to generate test data and integrate with APIs Advanced prompt techniques for automation engineers Chapter 7: Applying Generative AI to API Testing Generate API Payloads using AI Create POJO Classes from JSON responses Automatically generate JSON Schema from API responses Add assertions to API tests with AI-generated code Convert data formats (JSON to CSV and vice versa) Build utility methods to read data from JSON, CSV, and XML files using AI Chapter 8: AI in SQL and Database Testing AI-powered SQL Query Generation Query optimization and performance tuning Data integrity and validation checks Verify query accuracy using AI Schema validation using AI-generated prompts Ensure data consistency during data migration tasks with AI assistance Chapter 9: Mastering GitHub Copilot for Testers and Automation Engineers Install and set up GitHub Copilot Generate meaningful commit messages automatically Summarize code changes with AI assistance Use Copilot to suggest bug fixes and improvements Generate sample test data directly in your IDE Automatically rewrite code to match desired styles or patterns Use Copilot to generate documentation for your test methods Chapter 10: AI Agents Limitations of LLM's How AI Agents overcome limitations of LLM's LLMs Vs AI Agents Chapter 11: Exploring testRigor (Generative AI Based Test Automation Tool) Chapter 12: Playwright MCP (Model Context Protocol) with GitHub Copilot Integration What is Prompting? What are LLM's? Limitations of LLM's What is Agent? Understanding Model Context Protocol (MCP) Working with GitHub Copilot What is MCP? Playwright MCP configuration in VSCode Creating test context and test cases. Generating Web/UI & API tests using Copilot & MCP VSCode playwright test extension for managing tests. Vibe coding "Many more AI concepts are in the pipeline. Stay tuned!" Take the next step in your QA career and become an AI-empowered tester. Enroll today and transform the way you test!

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