Lecture 62: Introduction to ContextQA - Upload requirements to

Learn AI Tools & Build AI Agents for Software Testing

Section 1: Course Introduction - Content overview

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

Lecture 3: Why Prompt Engineering matters? - Mastering it is an Art. (6:04)
Lecture 4: Adding Constraints to leverage Zero shot prompting for better AI results (9:19)
Lecture 5: Practice scenarios for crafting prompts better & Few shot prompting technique (6:09)
Lecture 6: 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 7: What are tokens? Why it matters when interacting with AI Models (6:59)
Lecture 8: Understand how Context Window Limit works - Tips to save tokens (6:53)
Lecture 9: Generating Test Plan for the Project business requirements using AI (13:15)
Lecture 10: Generating Test Cases for the requirements using AI (10:11)
Lecture 11: Generating Test Strategy (Shift Left Testing) with the given Test cases using AI (9:11)
Lecture 12: Generate Test Data combinations for the given tests using AI (10:19)
Lecture 13: Privacy & Security of AI Applications- How companies are evolving to adapt (5:18)

GitHub Copilot Fundamentals — Ask, Agent, Planning Modes & IDE Installs

Lecture 14: Introduction to GitHub Copilot and it features for AI Integration inside editors (9:10)
Lecture 15: Demonstration of Ask & Agent modes in Copilot with in VS code with demo examples (10:01)
Lecture 16: Understanding how Planning mode helps for research and suggest design solutions (7:54)
Lecture 17: GitHub copilot install on -IntelliJ & Pycharm Editors for Java & Python Projects (5:10)
Lecture 18: GitHub copilot install steps with overview on -Eclipse Editors for Java project (5:41)
Lecture 19: 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 20: What is MCP? How this MCP help an LLM to be super powerful (28:09)
Lecture 21: Resources to download
Lecture 22: Build Agent which automates web browser using Playwright/Selenium MCP Servers (14:36)
Lecture 23: Debugging steps when there are failures in configuring MCP servers
Practice Role Play 3: Justifying the use of MCP to Your Project Manager
Lecture 24: Resource
Lecture 25: Build Agent which can extract data from SQL database by framing complex queries (23:40)
Lecture 26: Hands-On Practice Resources for Testing Skills
Lecture 27: Build Agent which can perform API Testing & talk to local File systems for data (18:29)
Lecture 28: Build Agent which can read/write to excel file for any given scenario (10:40)
Lecture 29: Setting up Playwright MCP configuration with in VS Code and generate Tests (10:53)

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

Lecture 30: Introduction to Agentic AI - What problems we are solving here? -Action Plan (9:51)
Lecture 31: Introduction to Claude Code Skill System - Problem statement (5:50)
Lecture 32: Download the code base used in this section
Lecture 33: Install Claude code & Claude for Chrome and get started with /init file (10:58)
Lecture 34: Tip - Good to know
Lecture 35: Understand Knowledge Skills & Agent Skills - When to use with demo example (7:30)
Lecture 36: Create Skill docs for EventHub Application & Understand how they are designed (13:14)
Lecture 37: Avoid Context Bloat: Use Smart References for Accurate AI Responses (6:07)
Lecture 38: The Magic of Agent creating Test Scenarios by reading the Project domain doc (9:49)
Lecture 39: The Magic of Agent Creating Test Strategy to push tests into different layers (15:45)
Lecture 40: Create Skills for Playwright best Practices and then build Agent to write Tests (15:01)
Lecture 41: Demo: Agent Running Tests and Fixing Failed Tests by Referring to Domain Docs (16:50)
Lecture 42: Tip - Good to know
Lecture 43: 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 44: Important Note
Lecture 45: What Are GitHub Copilot Custom Agents? Build Your First Custom Agent (11:06)
Lecture 46: 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 47: With AI Agents Implement CI/CD using GitHub Actions & push code to Remote GIT (7:02)
Lecture 48: Demo of CI/CD in Action with simple prompt solution using Claude code AI Agent (11:26)
Lecture 49: Implement Docker Solution to containerize the Tests in local with AI Agent (15:40)
Lecture 50: Practical Conversations between Manager & QA Person on building Devops solutions

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

Lecture 51: What is n8n? Overview of Business Process Automation worflows (8:15)
Lecture 52: How n8n revolutionized with AI Agents encapsulation - Demo overview (7:29)
Lecture 53: Create n8n AI Agent to read the Google Sheet and identify the bugs in New Status (9:08)
Lecture 54: Setting up Jira cloud and Create a Project for AI Agent setup (6:22)
Lecture 55: Plugin Jira tool to AI Agent and create e2e n8n Workflow for business usecase (13:38)
Lecture 56: 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 57: Using Generative AI for API Testing- parsing Json responses (9:37)
Lecture 58: Generating POJO classes for complex Json and generate Java methods using AI (11:12)
Lecture 59: Generating API tests in Cypress & Playwright with the given API contract (10:15)
Lecture 60: Generating complex SQL Queries for Database tables using AI (9:50)
Quiz 1: Check your knowledge on MCP, LLM and AI Agents

Section 11: Agentic AI - Introduction to LLM Powered Test Automation Tools

Lecture 61: Gen AI Testing vs LLM Powered Automation testing tools - overview (5:15)
Lecture 62: Introduction to ContextQA - Upload requirements to generate Testcases (12:27)
Lecture 63: Links for Handson with ContextQA Platform
Lecture 64: ContextQA - Turning Manual Test into executable Test Automation steps with demo (8:27)
Lecture 65: Deep dive into ContextQA Features - Environment variables, Test Suites -Part 1 (8:04)
Lecture 66: Organizing tests and create Release Test Plan powered by Test execution on Cloud (7:50)
Lecture 67: Context QA Browser plugin to record Browser Actions & generate executable tests (11:33)
Lecture 68: TestMu AI Demo - Quality Engineering Platform in the era of AI

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

Lesson content locked

If you're already enrolled, you'll need to login.