Practice Role Play 3: Justifying the use of MCP to Your Project

GenAI & AI Agents for QA Automation | Copilot & Claude code

Section 1: Introduction to AI Testing terminologies

Lecture 1: Introduction to AI Terminologies & Course Agenda - 1 (11:57)
Lecture 2: Course Agenda Continued.. - Part 2 (4:00)
Lecture 3: Privacy & Security of AI Applications- How companies are evolving to adapt (5:18)

Section 2: Prompt Engineering - Understand 3 C's - Context, Constraints, Clarity

Lecture 4: Important Note
Lecture 5: Why Prompt Engineering matters? - Mastering it is an Art. (6:04)
Lecture 6: Adding Constraints to leverage Zero shot prompting for better AI results (9:19)
Lecture 7: Practice scenarios for crafting prompts better & Few shot prompting technique (6:09)
Lecture 8: Chain of thought prompting - Let AI detail us on how it is thinking (6:29)

Section 3: Understand Tokens & Generate Test Plan, Test Cases, Test Strategy using AI

Lecture 9: What are tokens? Why it matters when interacting with AI Models (6:59)
Lecture 10: Understand how Context Window Limit works - Tips to save tokens (6:53)
Lecture 11: Generating Test Plan for the Project business requirements using AI (13:15)
Lecture 12: Generating Test Cases for the requirements using AI (10:11)
Lecture 13: Generating Test Strategy (Shift Left Testing) with the given Test cases using AI (9:11)
Lecture 14: Generate Test Data combinations for the given tests using AI (10:19)

Section 4: Effortlessly Generate Automation Code for Selenium, Playwright, Cypress using AI

Lecture 15: Introduction to GitHub Copilot and it features for AI Integration inside editors (9:10)
Lecture 16: Demonstration of Ask & Agent modes in Copilot with in VS code with demo examples (10:01)
Lecture 17: Understanding how Planning mode helps for research and suggest design solutions (7:54)
Lecture 18: GitHub copilot install on -IntelliJ & Pycharm Editors for Java & Python Projects (5:10)
Lecture 19: GitHub copilot install steps with overview on -Eclipse Editors for Java project (5:41)
Lecture 20: GenAI Github copilot plugin for Selenium Java Frameworks within Intellij Editor (30:46)

Section 5: Intro to Model Context Protocol (MCP) Servers & Build Agents with MCP tooling

Lecture 21: What is MCP? How this MCP help an LLM to be super powerful (28:09)
Lecture 22: Resources to download
Lecture 23: Build Agent which automates web browser using Playwright/Selenium MCP Servers (14:36)
Practice Role Play 3: Justifying the use of MCP to Your Project Manager
Lecture 25: Resource
Lecture 26: Build Agent which can extract data from SQL database by framing complex queries (23:40)
Lecture 27: Hands-On Practice Resources for Testing Skills
Lecture 28: Build Agent which can perform API Testing & talk to local File systems for data (18:29)
Lecture 29: Build Agent which can read/write to excel file for any given scenario (12:49)
Lecture 31: Setting up Playwright MCP configuration with in VS Code (13:34)
Lecture 32: On single Prompt, create Test Automation Project and build test with locators (10:17)

Section 6: Building Agentic AI for Quality Engineering using Claude Code

Lecture 33: Introduction to Agentic AI - What problems we are solving here? -Action Plan (9:51)
Lecture 34: Introduction to Claude Code Skill System - Problem statement (5:50)
Lecture 36: Install Claude code & Claude for Chrome and get started with /init file (10:58)
Lecture 37: Tip - Good to know
Lecture 38: Understand Knowledge Skills & Agent Skills - When to use with demo example (7:30)
Lecture 39: Create Skill docs for EventHub Application & Understand how they are designed (13:14)
Lecture 40: Avoid Context Bloat: Use Smart References for Accurate AI Responses (6:07)
Lecture 41: The Magic of Agent creating Test Scenarios by reading the Project domain doc (9:49)
Lecture 42: The Magic of Agent Creating Test Strategy to push tests into different layers (15:45)
Lecture 43: Create Skills for Playwright best Practices and then build Agent to write Tests (15:01)
Lecture 44: Demo: Agent Running Tests and Fixing Failed Tests by Referring to Domain Docs (16:50)
Lecture 46: Demo : Goal oriented Agentic Solution for the Test coverage anaylsis with report (11:19)

Section 7: Design GitHub Copilot Custom Agents & Cloud Agents for Automation Repositories

Lecture 47: Important Note
Lecture 48: What Are GitHub Copilot Custom Agents? Build Your First Custom Agent (11:06)
Lecture 49: GitHub Copilot Cloud Agents in Action: Running AI Agents on Demand (13:40)

Section 8: Build QA Devops Solutions (CI/CD, Docker, GitHub Actions) with AI Agents

Lecture 50: With AI Agents Implement CI/CD using GitHub Actions & push code to Remote GIT (7:02)
Lecture 51: Demo of CI/CD in Action with simple prompt solution using Claude code AI Agent (11:26)
Lecture 52: Implement Docker Solution to containerize the Tests in local with AI Agent (15:40)
Lecture 53: Practical Conversations between Manager & QA Person on building Devops solutions

Section 9: Build AI Agents with n8n Automation workflows - Demo examples

Lecture 54: What is n8n? Overview of Business Process Automation worflows (8:15)
Lecture 55: How n8n revolutionized with AI Agents encapsulation - Demo overview (7:29)
Lecture 56: Create n8n AI Agent to read the Google Sheet and identify the bugs in New Status (9:08)
Lecture 57: Setting up Jira cloud and Create a Project for AI Agent setup (6:22)
Lecture 58: Plugin Jira tool to AI Agent and create e2e n8n Workflow for business usecase (13:38)
Lecture 59: Building a Public Chat Interface to Interact with n8n AI Workflow via Webhook (5:09)

Section 10: Generating Automation Code for API Applications using AI

Lecture 60: Using Generative AI for API Testing- parsing Json responses (9:37)
Lecture 61: Generating POJO classes for complex Json and generate Java methods using AI (11:12)
Lecture 62: Generating API tests in Cypress & Playwright with the given API contract (10:15)
Lecture 63: Generating complex SQL Queries for Database tables using AI (9:50)
Quiz 1: Check your knowledge on MCP, LLM and AI Agents

Section 11: Introduction to AI Powered exclusive Testing tools - Low Code Automation

Lecture 65: Introduction to TestRigor AI tool and its features on creating Tests using AI (12:30)
Lecture 64: Understanding the capabilities of AI powered Testing tools (13:06)
Lecture 66: Understanding Scripting language of Test Rigor with a demo test (21:25)
Lecture 67: Self Healing capabilities to fix tests using AI powered testing tools (9:13)

Section 12: Privacy first, offline LLM Models to effortlessly handle your Project Domain

Lecture 68: Setup Custom LLM with local project documents to get Domain related answers (9:13)
Lecture 69: Generating Automatic Test cases on fly with the english requirements (13:44)

Section 13: Final words - Interview Questions & Future of AI in QA Space

Lecture 70: Interview Questions to crack your next Job - Recap the topics (28:53)
Lecture 71: Introduction to GPT4All - and how it works offline to generate results (8:27)
Lecture 72: How AI can be your best buddy for coding practice and implementation (8:26)
Lecture 73: Future proof your QA-AI Skills - What next? (3:53)
Lecture 74: Thankyou Note with future updates Plan
Lecture 75: Bonus Lecture

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