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The QA Course That Puts You Five Years Ahead of Everyone Else.

Learn to test AI applications, validate LLMs, detect model bias, and build autonomous QA workflows. The most advanced course in India's QA training landscape.

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AI is not just changing how we test software — it’s changing what software is. This course has two dimensions: first, how to use AI to become a dramatically more productive QA engineer; second, and more importantly, how to test the AI systems themselves. You’ll learn to evaluate LLM responses, detect hallucinations and bias, test RAG pipelines, break AI systems with adversarial prompts, and audit them for ethical compliance. If you’re a senior QA engineer, this is where your next promotion lives.

Syllabus

Part 1 — Become an AI-Powered QA Engineer (Phases 1–4)

Use AI tools to design smarter tests, write automation faster, eliminate flakiness, and debug with precision.
Phase 1: GenAI Fundamentals & Prompt Engineering for QA

Modules

GenAI Fundamentals

Prompt Engineering 101

Advanced Prompting

QA Personas

Prompt Management

Phase 2: AI-Assisted Test Design & Manual Testing

Modules

AI Test Scenario Gen

Test Case Optimization

Edge Case Discovery

Test Data Generation

AI in Bug Reporting

Phase 3: AI-Powered Automation with Copilot & Cursor

Modules

AI for Code Logic

Copilot/Cursor Basics

Script Generation

AI Code Review

Fixing Flaky Tests

Phase 4: Visual Testing, Self-Healing & AI Analytics

Modules

AI Visual Validation

Self-Healing Tests

AI API Testing

AI Test Analytics

Part 1 Project

Part 2 — Test AI Applications Like an Expert (Phases 5–8)

Learn to validate LLMs, evaluate RAG systems, test for bias and safety, and audit AI agents — skills that don’t exist in any other QA course in India.
Phase1: LLM Internals — How AI Applications Work

Modules

LLM Internal Mechanics

RAG Systems 101

Prompt Injection Intro

Model Hallucinations

Determinism vs Stochastic

Phase2: LLM Evaluation — Ragas, DeepEval & Benchmarking

Modules

Evaluation Metrics

Ragas Framework

DeepEval Basics

Human-in-the-loop

Model Benchmarking

Phase3: AI Safety — Jailbreaking, Bias, PII & Ethics

Modules

Jailbreaking & Safety

Bias & Toxicity

PII & Privacy Testing

Adversarial Testing

Ethical AI Compliance

Phase4: Agentic AI Testing — Multi-Agent & Autonomous QA

Modules

AI Agent Workflows

Multi-agent Testing

Latencey & Performance

Cost & Token Audit

Final Capstone

Outcome

This course equips you with two sets of rare, in-demand skills:
After Part 1 — AI-Powered Testing, you will be able to:
  • Generate test cases from requirements using prompt engineering and GitHub Copilot
  • Build automation scripts 3× faster with AI-assisted code generation
  • Implement self-healing test frameworks that adapt to UI changes automatically
  • Use AI to detect flaky tests, summarise logs, and root-cause failures in minutes
After Part 2 — Testing AI Applications, you will be able to:
  • Evaluate LLM outputs for accuracy, hallucination, and consistency using Ragas and DeepEval
  • Test RAG pipelines for retrieval quality and response faithfulness
  • Execute adversarial testing — prompt injection, jailbreaking, and boundary attacks
  • Audit AI models for bias, toxicity, PII leakage, and ethical compliance
  • Test multi-agent AI workflows for latency, cost efficiency, and reliability
Career roles you’ll be ready for: AI QA Engineer, LLM Quality Engineer, Senior SDET, QA Tech Lead (AI Systems), AI Safety Tester
This is not just a course upgrade — it is a career category change.

Tools

Selenium

Java

Maven

Cucumber

REST Assured

Docker

Jenkins

GitHub Copilot

FAQs

This course is designed for experienced QA engineers, test automation engineers, and software testers who want to transition into AI-driven testing and modern QA practices.

No prior AI knowledge is required. The course starts with AI and LLM fundamentals and gradually moves toward advanced AI testing concepts and tools.

You will learn Prompt Engineering, AI application testing, LangChain for RAG systems, n8n automation workflows, AI-assisted automation tools, and modern QA frameworks.

Yes. The program includes real-world AI SaaS testing projects, allowing you to practice AI testing strategies, automation workflows, and model validation techniques.

You’ll use Ragas and DeepEval for LLM evaluation, LangChain for RAG pipeline testing, n8n for automation workflow validation, and custom adversarial prompt libraries for safety testing. These are production tools used by AI QA teams at frontier AI companies.
Yes, with one caveat — this is an advanced course and we recommend at least 2 years of QA experience before joining. Part 1 starts with GenAI fundamentals and requires no prior AI knowledge. By the time you reach Part 2, you’ll have the conceptual foundation to engage with LLM testing confidently.
Most courses add a 2-hour ‘AI tools’ chapter at the end. This is an entirely separate, dedicated curriculum. Part 1 is 4 full phases of AI-powered testing productivity. Part 2 is 4 phases of testing actual AI systems — a skill that literally does not exist in most other QA courses in India. The curriculum is built on the tooling and evaluation frameworks used by AI companies, not just general productivity tips.

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