The QA Course That Puts You Five Years Ahead of Everyone Else.
AI QA roles are growing 48% faster than industry average with a $145K median salary for senior AI test engineers in the US (AI LLM Testing, Feb 2026). The most advanced course in India's QA training landscape.
Syllabus
Part 1 — Become an AI-Powered QA Engineer (Phases 1–4)
Modules
GenAI Fundamentals
Prompt Engineering 101
Advanced Prompting
QA Personas
Prompt Management
Modules
AI Test Scenario Gen
Test Case Optimization
Edge Case Discovery
Test Data Generation
AI in Bug Reporting
Modules
AI for Code Logic
Copilot/Cursor Basics
Script Generation
AI Code Review
Fixing Flaky Tests
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)
Modules
LLM Internal Mechanics
RAG Systems 101
Prompt Injection Intro
Model Hallucinations
Determinism vs Stochastic
Modules
Evaluation Metrics
Ragas Framework
DeepEval Basics
Human-in-the-loop
Model Benchmarking
Modules
Jailbreaking & Safety
Bias & Toxicity
PII & Privacy Testing
Adversarial Testing / Red Teaming
Ethical AI / EU AI Act Compliance
Modules
AI Agent Workflows
Multi-agent Testing
Latency & Performance
Cost & Token Audit
Final Capstone
Outcome
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 10× 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
- Trace and debug RAG pipelines using LangSmith observability
- Benchmark LLM cost-per-query across GPT-4o, Claude, and Llama for business decisions.
- Audit AI systems for EU AI Act compliance — risk categorisation and documentation requirements
Tools
GitHub Copilot
Cursor IDE
LangChain
n8n
Playwright
Applitools
ChatGPT
Claude
Gemini
Who Should Enrol
Senior QA / automation engineer →
You know automation. Now add the layer that earns a 30-40% salary premium — testing AI itself.
SDET →
Your automation skills are the foundation. Add LLM evaluation, adversarial testing, and EU AI Act auditing — skills that no standard SDET course teaches.
QA lead →
Your team is already behind if they can't test an LLM. This course gives them — and you — the framework to close that gap in 2 Months.
Developer / ML engineer →
Add the formal QA methodology most AI/ML teams skip — you already build the models, learn to test them like a QA engineer would.
Market Growth
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 GitHub Copilot, Cursor IDE, Playwright (AI-generated), Ragas (RAG evaluation), DeepEval (LLM unit testing), LangChain + LangSmith (RAG pipelines + tracing), n8n (workflow automation), Applitools (visual AI), ChatGPT / Claude / Gemini, custom adversarial prompt libraries, EU AI Act compliance 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, LangSmith for LLM tracing and observability, Confident AI as the production-grade evaluation platform, and custom adversarial prompt libraries for red-teaming. These are the tools used at frontier AI companies (Anthropic, OpenAI, Cohere) for safety and quality evaluation.
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.