Next-Gen AI Automation Testing with Playwright & TypeScript – Live Training

Next-Gen AI Automation Testing with Playwright & TypeScript – Live Training

(AI Testing Fundamentals | Playwright with TypeScript | Prompt Engineering | LLM Validation | API & UI Testing | MCP Integration | CI/CD with GitHub Actions | Real-Time AI Automation Projects)

 

Step into the future of software testing with our advanced AI-driven automation testing program, designed for professionals and aspiring testers who want to master modern AI testing practices using Playwright and TypeScript.

This course provides complete hands-on training in AI testing foundations, Large Language Model (LLM) validation, prompt engineering, AI response verification, automation framework design, API and UI automation, JSON schema validation, AI scoring systems, CI/CD integration, and real-time project implementation.

Learners will gain practical expertise in building robust automation frameworks for AI-powered applications, validating intelligent system responses for accuracy, relevance, consistency, safety, and performance, while implementing advanced testing strategies used in real-world AI products.

The program includes real-time implementation of AI chatbot testing and AI resume analyzer automation projects, helping students develop practical experience in handling AI workflows, prompt validation, response scoring, structured output verification, file automation, API testing, reporting, and intelligent test execution.

Course syllabus:

Module 1: AI Testing Foundation

🔹Introduction to AI QA Engineer

• • What is AI QA?
  • SDET vs AI SDET
  • Traditional Testing vs AI Testing
  • AI application ecosystem

🔹AI Testing Basics

• • What is LLM?
  • Prompt, Token, Context
  • Hallucinations
  • Deterministic vs Non-deterministic responses

🔹AI Testing Strategy

• • Accuracy testing
  • Relevance testing
  • Safety testing
  • Bias testing
  • Response consistency testing

🔹Advanced Playwright Recap

• • Playwright architecture
  • Test runner
  • Fixtures
  • Hooks
  • Advanced locators

🔹TypeScript for Frameworks

• • Interfaces
  • Types
  • Classes
  • Async/Await
  • Utility methods

🔹Framework Setup

• • Playwright installation
  • TS configuration
  • Environment variables
  • Folder structure
  • Base framework creation

🔹Mini Practice Project

• • First AI test
  • Capture AI response
  • Basic assertions
  • HTML report generation

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Module 2: Advanced Playwright Framework

🔹Page Object Model

• • POM design
  • Reusable methods
  • Login page
  • Chat page

🔹 Component Object Model

• • Chat components
  • Upload components
  • Common reusable UI modules

🔹 Test Data Management

• • JSON test data
  • Prompt datasets
  • Dynamic data generation
  • Negative datasets

🔹 Fixtures & Hooks

• • Custom fixtures
  • Global setup
  • Before/After hooks
  • Cleanup strategy

🔹 Assertion Strategy

• • UI assertions
  • API assertions
  • Soft assertions
  • Custom assertions

🔹 Debugging Playwright

• • Trace Viewer
  • Screenshots
  • Videos
  • Console logs
  • Network debugging

🔹 Weekly Framework Project

• • Framework integration
  • Reporting
  • Folder standards
  • Final review

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Module 3: Prompt Engineering for QA

🔹 Prompt Engineering Basics

• • Good vs bad prompts
  • Prompt structure
  • Role-based prompts
  • Clear instruction writing

🔹 Requirement to Test Cases

• • Requirement analysis
  • AI-generated test cases
  • Positive scenarios
  • Negative scenarios
  • Edge cases

🔹 Prompt Testing

• • Prompt injection testing
  • Jailbreak testing
  • Same prompt validation
  • Multi-prompt testing

🔹 AI Response Validation

• • Exact match validation
  • Keyword validation
  • Regex validation
  • Length validation

🔹 JSON Schema Validation

• • Structured AI outputs
  • Schema validation
  • Required fields
  • Data type checks

🔹 AI Scoring System

• • Accuracy scoring
  • Relevance scoring
  • Safety scoring
  • Completeness scoring
  • Threshold logic

🔹 Weekly Assignment

• • Prompt dataset creation
  • Response validation
  • AI scoring
  • Summary report generation

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Module 4: Real-Time Project 1: AI Chatbot Testing

🔹 Chatbot Application Overview

• • Chatbot workflows
  • User journeys
  • Risk areas
  • Test strategy

🔹 Chatbot UI Automation

• • Open chatbot
  • Send prompts
  • Capture AI responses
  • Validate UI rendering

🔹 Functional Testing

• • FAQ prompts
  • Invalid prompts
  • Empty prompts
  • Support workflows

🔹 Context Memory Testing

• • Multi-turn conversation
  • Follow-up validation
  • Context retention
  • Context reset testing

🔹 Negative AI Testing

• • Unsafe prompts
  • Abusive prompts
  • Prompt injection
  • Harmful response validation

🔹 Chatbot API Testing

• • Prompt API
  • Response validation
  • Status code validation
  • Response time testing

🔹 Project Completion

• • Test suite integration
  • Failure screenshots
  • Reports
  • Final review

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Module 5: Real-Time Project 2: AI Resume Analyzer Testing

🔹 Resume Analyzer Overview

• • Resume upload flow
  • AI scoring system
  • Skill extraction
  • JD matching

🔹 File Upload Automation

• • PDF upload
  • DOCX upload
  • Invalid file testing
  • File size validation

🔹 AI Output Validation

• • Name extraction
  • Skill extraction
  • Score validation
  • Missing skills validation

🔹 JD Matching Testing

• • Resume vs JD validation
  • Match score testing
  • Recommendation validation

🔹 Resume Analyzer API Testing

• • Upload API
  • Analyze API
  • Error handling
  • Response validation

🔹 Report Download Testing

• • Download reports
  • File existence validation
  • Content validation
  • Format validation

🔹 Project Completion

• • End-to-end automation
  • AI scoring
  • JSON schema validation
  • Report generation

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Module 6: AI Framework + CI/CD + Interview Preparation

🔹 AI Utility Functions

• • Prompt builder
  • Validator utilities
  • Score calculator
  • Report formatter

🔹 LLM-as-a-Judge

• • AI validating AI
  • Evaluation prompts
  • Pass/fail reasoning
  • Limitations

🔹 Flaky AI Test Handling

• • Retry strategy
  • Threshold validation
  • Golden dataset
  • Stable assertions

🔹 CI/CD Integration

• • GitHub Actions
  • Automated test runs
  • Report publishing
  • Failure screenshots

🔹 AI Debugging Workflow

• • Trace analysis
  • Screenshot analysis
  • Console debugging
  • Bug summary generation

🔹 Playwright MCP Introduction

• • MCP basics
  • AI-assisted browser testing
  • Exploratory AI testing
  • MCP limitations

🔹 Framework Cleanup

• • Naming standards
  • README creation
  • Code cleanup
  • Folder review

🔹 Resume Preparation

• • AI QA resume points
  • Project explanation
  • GitHub portfolio
  • LinkedIn optimization

🔹 Interview Preparation

• • AI testing interview questions
  • Playwright advanced questions
  • Prompt engineering questions
  • Framework discussions

🔹 Final Demo Day

• • Chatbot project demo
  • Resume analyzer demo
  • Framework walkthrough
  • Mock interview

FAQ’s –Next-Gen AI Automation Testing with Playwright & TypeScript

1. What is this course about?
This course covers AI-driven automation testing using Playwright, TypeScript, Prompt Engineering, LLM validation, API & UI automation, and real-time AI projects.

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2. Who can enroll in this course?
Manual testers, automation engineers, SDET aspirants, developers, freshers, and anyone interested in AI automation testing can enroll.

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3. Do I need prior Playwright knowledge?
Basic automation knowledge is helpful, but the course covers Playwright fundamentals to advanced framework development.

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4. Will I work on real-time projects?
Yes, students will build AI Chatbot Testing and AI Resume Analyzer Automation Projects with practical implementation.

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5. What technologies will I learn?
You will learn Playwright, TypeScript, Prompt Engineering, LLM Validation, JSON Schema Validation, MCP Integration, API Testing, and GitHub Actions CI/CD.

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6. Will I get hands-on practice?
Yes, the course includes practical coding sessions, assignments, framework building, and project implementation.

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7. Will interview preparation be included?
Yes, the course includes resume preparation, project explanation guidance, mock interview preparation, and advanced AI QA interview questions.

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8. Will I learn CI/CD integration?
Yes, you will learn GitHub Actions integration for automated test execution and report publishing.

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9. What projects will I complete?
You will complete AI automation framework development, chatbot testing automation, resume analyzer testing, scoring utilities, and reporting projects.

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10. What career opportunities after this course?
You can apply for roles like AI QA Engineer, Automation Test Engineer, SDET, Playwright Automation Engineer, and AI Testing Specialist.

How can I enroll for this course?

 

Enroll For Free Demo

OR

For any other details, Call me or Whatsapp me on +91-9133190573

Live Sessions Price:

For LIVE sessions – Offer price after discount is 300 USD 259 USD 99 USD Or 13000 INR 12900 INR 7900 Rupees

Sample Course Completion Certificate:

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Note:

To maintain the quality of our training and ensure a smooth learning experience for all participants, we do not allow batch repetition or switching between courses.

To reiterate, moving from one course to another or shifting from one trainer to another (even if it is the same course) is not possible. Changing batches or trainers in any form is strictly not permitted.

We request all learners to attend the scheduled sessions regularly and make the most of their learning journey. Thank you for your understanding and continued support.