What you'll learn:
- Learn how to use Gen AI LLM's effectively to maximize your QA Productivity with smart prompt engineering skills
- Understand how to Generate & optimize the Test code into framework standards with Gen AI Plugins such as Github copilot etc
- Understand how MCP Servers work & how they can boost LLM to be specialized Automation AI Agents
- Get overview of AI Powered Testing tools in current market and their capabilities for revolutionizing Test Automtion
- Learn how AI Agents work and how they can be used to perform Codeless browser Automation with help of LLM's
- Learn the power of AI at terminal level using Claude code with practical Selenium Framework example
- Learn n8n basics & build Automation workflows powered by AI Agents
- Understand how to work with offline LLM's with full privacy and customize the LLM as per your project requirements
- Learn generating Test Artifacts in fly such as TestPlan, Testcases, TestData, Bug templates for given Business requirements
Course last Updated -March 2026 with topic :Claude Code Skill System workflows
AI is no longer just a buzzword in software testing. It is becoming a real productivity multiplier for QA engineers, automation testers, and quality engineering teams. This course is built to help you move beyond theory and learn how to actually use AI tools, AI agents, Claude Code, GitHub Copilot, MCP servers, n8n workflows, and low-code AI testing platforms in practical testing scenarios.
We begin with the fundamentals by covering AI testing terminology, privacy and security considerations, prompt engineering, token concepts, context window limitations, and techniques to generate better AI responses. You will learn how to use AI effectively for creating test plans, test cases, test strategies, and test data combinations from business requirements.
The course then moves into hands-on implementation. You will see how GitHub Copilot can help fix code issues and speed up automation development inside real coding environments. From there, we dive deep into Model Context Protocol (MCP) and show how to build powerful automation agents that can interact with browsers, APIs, SQL databases, local files, Excel sheets, and Git workflows.
A major highlight of this course is Building Agentic AI for Quality Engineering with Claude Code. You will learn how to work with Claude Code skill systems, create domain knowledge skills, design agent skills, avoid context bloat with smart references, and build agents that can understand project documentation, generate test scenarios, design test strategies, write tests, run tests, and even help fix failed tests by referring back to domain docs.
You will also learn how sub-agents, multi-agent collaboration, and agentic AI solutions can be used to break down complex QA responsibilities into specialized roles. In addition, the course demonstrates how to build AI agents with n8n automation workflows, integrate with tools like Jira and Google Sheets, and create practical business-oriented automation flows.
The learning does not stop there. You will also explore AI-powered API testing, AI-exclusive low-code testing tools, self-healing automation concepts, and privacy-first offline LLM setups to securely handle project domain knowledge in enterprise-friendly environments.
This course is ideal for:
QA Engineers
Automation Testers
SDETs
Manual Testers moving into AI-powered QA
Engineers curious about Claude Code, Copilot, MCP, n8n, and Agentic AI for testing
If you want to understand where QA is heading and learn how to boost testing productivity with practical AI-driven workflows, this course gives you a complete roadmap with demos, examples, and modern tools that are shaping the future of quality engineering.
