Advanced GenAI for QA Engineers
Leverage AI to accelerate test creation, maintenance, and defect analysis.
Why Choose Us?
Introduction to Generative AI
Understand the fundamentals of Generative AI, how modern AI models work
Large Language Models (LLMs)
Learn how models like GPT, Claude, Gemini, and Llama process language
Prompt Engineering
Master the techniques of writing effective prompts to obtain accurate
Become a Future-Ready AI-Powered QA Engineer Master Generative AI from a Test Automation Engineer’s perspective and confidently transition into high-demand AI-focused QA roles.
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
What You'll Learn
- How LLMs work (conceptual clarity, no heavy math)
- Tokens, embeddings, vector databases explained practicaly
- AI application architecture overview
- Common AI failure points
- Enterprise AI risks and testing chalenges
Learn Generative AI concepts simplified for testers
- Writing deterministic and controled prompts
- Preventing hallucinations
- Prompt versioning and governance
You will l 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
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
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
You wi l learn how to validate model behavior after retraining cycles.
- Reinforcement Learning explained simply
- What is RLHF (Reinforcement Learning with Human Feedback)
- Feedback loop validation
Focus: Ensuring model improvement does not introduce hidden regressions.
Outcome of This Course
You will gain practical knowledge equivalent to working on 2–3 real AI testing implementations
You will understand how to test LLM-based and AI-powered applications
You wil confidently design AI validation strategies and regression approaches
You will be able to integrate GenAI into automation frameworks
You wi l be prepared to move into AI QA Engineer / AI Test Architect roles
Course Highlights
100% Practical, Hands-On Sessions
Live AI Testing Workshops using real-world scenarios
Enterprise-Level AI Validation Use Cases
Build AI-powered QA Utilities
Training delivered by a trainer with 15+ years of industry experience
Dedicated Doubt Clarification Session
Course Duration
Batch Details
- • Weekday Batches
- • Weekend Batches