How to implement CI/CD with GitHub Actions and push code to Remote GIT

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

Section 1: Introduction to AI Testing terminologies

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

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

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

Understand Tokens & Generate Test Plan, Test Cases, Test Strategy using AI

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

Effortlessly Generate Automation Code for Selenium, Playwright, Cypress using AI

GitHub Copilot extension for Visual Studio Code - Tips & Tricks

How this section covers AI plugins in multiple languages & editors (2:04)
Copilot - your AI Buddy to help coding inside VS code editor (19:33)
GenAI Github copilot plugin for Selenium Java Frameworks within Intellij Editor (36:24)

Section 4: Introduction to AI Powered exclusive Testing tools

Understanding the capabilities of AI powered Testing tools (13:06)
Introduction to TestRigor AI tool and its features on creating Tests using AI (12:30)
Generating Automatic Test cases on fly with the english requirements (13:44)
Understanding Scripting language of Test Rigor with a demo test (21:25)
Self Healing capabilities to fix tests using AI powered testing tools (9:13)

Build Automation AI Agents with Model Context Protocol (MCP) Servers

What is MCP? How this MCP help an LLM to be super powerful (28:09)
Resources to download
Build Agent which automates web browser using Playwright/Selenium MCP Servers (14:36)
Build Agent which can extract data from SQL database by framing complex queries (23:40)
Build Agent which can perform API Testing & talk to local File systems for data (18:29)
Build Agent which can read/write to excel file for any given scenario (12:49)
Setting up Playwright MCP configuration with in VS Code (13:34)
On single Prompt, create Test Automation Project and build test with locators (10:17)
Github MCP Server - Magic of commiting files and Code push to Git with Prompts (9:34)

Connect the dots of Sub Agents, Multi Agent collaboration, Agentic AI Solutions

Create Custom chatmodes (Sub Agents) with in Github Copilot Agent mode (10:19)
Advantages of Multi Specialized Agents than Single Agent holding multiple roles (4:42)
What are Agentic AI Solutions? How different they are from AI Agents (10:17)

Build AI Agents with n8n Automation workflows - Demo examples

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

Section 5: Generating Automation Code for API Applications using AI

Using Generative AI for API Testing- parsing Json responses (9:37)
Generating POJO classes for complex Json and generate Java methods using AI (11:12)
Generating API tests in Cypress & Playwright with the given API contract (10:15)
Generating complex SQL Queries for Datbase tables using AI (9:50)

AI Agents with browse-use - Build AI Driven Test Automation Framework

Welcome to AI Agents World! (2:01)
What are AI Agents? How Browser-Use enables Agents for browser automation (10:05)
browser-use demo & Setup Python & Pycharm IDE to get started with AI Agents (12:33)
Understand creating AI Agent object with task goal & LLM Intelligence (10:41)
Setup LLM using Gemini Models and run first AI Agent task for browser automation (12:58)
Pyndantic Data models to convert AI Agents raw responses to the Test Assertions (11:29)
How to create custom controller actions to inject playwright code as Plan B (20:39)

Building Agentic AI for Quality Engineering with Claude Code

Introduction to Claude Code Skill System - Problem statement (5:50)
Download the code base used in this section
Install Claude code & Claude for Chrome and get started with /init file (10:58)
Tip - Good to know
Understand Knowledge Skills & Agent Skills - When to use with demo example (7:30)
Create Skill docs for EventHub Application & Understand how they are designed (13:14)
Avoid Context Bloat: Use Smart References for Accurate AI Responses (6:07)
The Magic of Agent creating Test Scenarios by reading the Project domain doc (9:49)
The Magic of Agent Creating Test Strategy to push tests into different layers (15:45)
Create Skills for Playwright best Practices and then build Agent to write Tests (15:01)
Demo: Agent Running Tests and Fixing Failed Tests by Referring to Domain Docs (16:50)
Tip - Good to know
Demo : Goal oriented Agentic Solution for the Test coverage anaylsis with report (11:19)

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

How to implement CI/CD with GitHub Actions and push code to Remote GIT (7:02)
Demo of CI/CD in Action with simple prompt solution using Claude code AI Agent (11:26)
Implement Docker Solution to containerize the Tests in local with AI Agent (15:40)
Extended conversation with Manager & Employee for knowledge purpose

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

Introduction to GPT4All - and how it works offline to generate results (8:27)
Setup Custom LLM with local project documents to get Domain related answers (9:13)

Section 6: Final words - Future of AI

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