GenAI for QA Automation

Become a High-Paid QA Engineer with Advanced GenAI Skills

120+ hrs of Intensive Training

150+ Successful Career Transitions

Why Choose Us?

Comprehensive curriculum

Courses covering all aspects of Automation Testing from basics to advanced techniques

Expert instructors

Learn from industry experts with real-world experience.

Hands-on experience

Practical skills through hands on training and real world projects

Master Advanced Generative AI concepts, AI-powered testing strategies, and practical GenAI tools to confidently transition into a high-paying AI-enabled QA Engineer role.

This course is designed with industry-first practices, real-time projects, and hands-on learning to make you job-ready, not just certificate-ready.

Who Is This Course For?

This course is ideal for:

Manual QA Engineers planning to future-proof their careers

Automation / SDET Engineers who want to integrate GenAI into their frameworks

QA Leads and Architects who need to validate AI-powered applications

Testers working in Banking, FinTech, Healthcare, or Enterprise domains where AI adoption is increasing

Professionals who want to stay ahead in the AI-driven testing landscape

Basic automation knowledge is helpful but not mandatory — we start with clear fundamentals and gradually move to advanced AI validation strategies.

What You'll Learn

How LLMs work (conceptual clarity, no heavy math)
Tokens, embeddings, vector databases explained practically
AI application architecture overview
Common AI failure points
Enterprise AI risks and testing challenges

Learn Generative AI concepts simplified for testers

Writing deterministic and controlled prompts
Preventing hallucinations
Prompt versioning and governance
Prompt regression testing
AI output validation strategies

You will learn how to treat prompts as testable and version-controlled assets.

Functional testing of AI systems
Non-functional testing for AI APIs
AI output validation models
Bias and fairness testing
Drift detection basics
Guardrails and response filtering validation
Risk-based AI testing approach

Focus: How do you test AI systems in real enterprise environments?

What is RAG and why enterprises use it
Embeddings and vector search (simplified for testers)
How retrieval pipelines work
Failure points in RAG systems
Testing document retrieval accuracy
Grounding validation vs hallucination detection

This is a critical enterprise AI testing skill.

Fine-Tuning vs Prompt Engineering
Risks introduced after model retraining
Base model vs fine-tuned model comparison
Regression strategy after model updates
Designing validation datasets

You will learn how to validate model behavior after retraining cycles.

Reinforcement Learning explained simply
What is RLHF (Reinforcement Learning with Human Feedback)
Feedback loop validation
Bias amplification risks
Testing model improvement without breaking stability

Focus: Ensuring model improvement does not introduce hidden regressions.

Creating golden datasets
Deterministic AI test cases
Automated response comparison strategies
AI scoring mechanisms (simplified precision/recall concepts)
Designing AI regression pipelines

This section prepares you for enterprise AI validation projects.

Prompt injection attacks
Jailbreak testing
Data leakage risks
Security validation for AI APIs
Token usage and cost optimization validation
Responsible AI testing practices

Critical for Banking and Enterprise environments.

AI-based test case generator
AI-based test data generator
AI-powered API scenario builder
AI-assisted defect analysis concept
AI-enhanced reporting insights

This makes you a tool builder, not just an AI user.

Outcome of This Course

You will gain practical knowledge equivalent to working on 2–3 real AI testing implementations (achieved through hands-on workshops and enterprise-style AI validation exercises)

You will understand how to test LLM-based and AI-powered applications

You will confidently design AI validation strategies and regression approaches

You will be able to integrate GenAI into automation frameworks

You will be prepared to move into AI QA Engineer / AI Test Architect roles

You will stand out from traditional automation engineers