Full Stack GenAI Testing & LLM Testing : Prompts, LLMs, RAG, Governance & Chatbots– Live Training Day3

Full Stack GenAI Testing & LLM Testing : Prompts, LLMs, RAG, Governance & Chatbots– Live Training

About The Instructor:

Jayanth is a highly experienced Automation and AI Testing professional with 10+ years of industry experience in Software Testing, Test Automation, and AI-driven applications. He has extensive expertise in AI Chatbot Testing, Large Language Models (LLMs), Enterprise RAG Architecture, and Playwright Automation. He has hands-on experience in LLM-based chatbot validation, prompt testing, response accuracy evaluation, and AI model behavior testing. Jayanth also trains students on Enterprise Retrieval-Augmented Generation (RAG) workflows, including document ingestion, embeddings, vector databases, semantic search, and AI response generation. In addition, he has practical knowledge in Playwright Automation using JavaScript/TypeScript, enabling testers to automate UI workflows for AI-driven applications. His training focuses on real-time projects, practical implementation, and industry best practices, helping students gain job-ready skills in AI testing and automation. With a strong passion for teaching and technology, Jayanth helps learners understand the complete AI testing lifecycle, from chatbot validation to automation testing of intelligent systems.

Live Sessions Price:

For LIVE sessions – Offer price after discount is 200 USD 159 USD 109 USD Or 15000 INR 12000 INR 8900 Rupees.

Enroll For Free Demo

OR

WhatsApp

 

Free Demo On:

Indian Timings: 1st June @ 9 PM – 10 PM (IST)/

U.S Timings: 1st June @ 11:30 AM – 12:30 PM (EST)/

U.K Timings: 1st June @ 4:30 PM – 5:30 PM (BST)

 

Class Schedule:

For Participants in India: Monday to Friday @ 9 PM – 10 PM (IST)

For Participants in the US: Monday to Friday @ 11:30 AM – 12:30 PM (EST)

For Participants in the UK: Monday to Friday @ 4:30 PM – 5:30 PM (BST)

 

What student’s have to say about Trainer :

👨 Rahul Kumar: Excellent training by Trainer Jayanth! The concepts of RAG architecture, vector databases, and embeddings were explained with real examples. The Enterprise RAG project helped me understand how AI systems work in real applications. 👨Arjun Reddy: I really liked the session on LLM evaluation and hallucination testing. The trainer explained how to validate AI responses and test chatbots effectively. Very useful for testers moving into AI testing. 👩 Priya Sharma: The course was very informative. I learned RAG architecture, embeddings, and LLM evaluation step by step. Trainer Jayanth explained everything clearly with real-time use cases. 👩 Ananya Gupta: I liked the hallucination testing and AI chatbot validation techniques. This training helped me understand how QA engineers can test AI applications. Very helpful course! 👨 Kiran Patel: The Playwright automation with chatbot testing was the best part of the course. Jayanth sir explained automation with AI tools in a simple and practical way. Highly recommended! 👨 Rohit Sharma:This course gave me a clear understanding of vector databases, embeddings, and Enterprise RAG workflow. The real-time project made learning very practical. 👩 Sneha Reddy: The Enterprise RAG real project was amazing. It helped me understand document ingestion, vector search, and response generation in AI systems. Great training! 👩Kavya Patel: Learning Playwright automation with AI chatbot testing was a great experience. The trainer explained automation workflows and testing strategies very well.

What Will I Learn by the End of This Course?

• Fundamentals of AI, ML, Deep Learning & Neural Networks
• Understanding of Transformers, NLP, LLMs & GPT Models
• Complete knowledge of Generative AI & Multi-modal AI Systems
• Real-world applications of AI in Healthcare, Banking, E-commerce & Chatbots
• Concepts of AI Agents, Agentic AI & MCP
• Difference between Traditional Testing vs AI Testing
• How to perform Prompt Testing & Prompt Engineering
• Techniques for Hallucination Testing, Bias Testing & Safety Validation
• Methods to evaluate AI responses using DeepEval & RAGAS Frameworks
• Understanding of RAG Architecture, Retrieval & Generation Evaluation
• Hands-on experience in AI-based Test Case Generation using ChatGPT/Copilot
• Creation of a Test Case Generator Tool using Azure OpenAI
• Knowledge of Responsible AI, AI Governance & Ethical AI Validation
• Enterprise-level GenAI Testing Strategies & Semantic Validation
• Practical implementation of Chatbot Testing using Playwright Automation
• Automation of chatbot interactions and AI Response Validation
• Industry-focused skills required for AI Testing & GenAI QA Careers

Salient Features:

• 25 Hours of Instructor-Led Live Training with real-time project demonstrations
• Lifetime Access to All Recorded Sessions for continuous learning and revision
• Industry-Recognized Course Completion Certificate upon successful completion

 

Who can enroll in this course?

• Manual Testing Professionals – Transition into AI Testing & GenAI QA
• Automation Test Engineers – Learn AI-powered Testing Techniques
• Software Developers – Understand LLMs, RAG, AI Agents & AI Validation
• QA Engineers – Master Prompt Testing, Hallucination Testing & AI Evaluation
• Freshers & Graduates – Start a career in the AI & Generative AI Industry
• DevOps / SRE / Cloud Professionals – Learn AI System Workflows & Testing
• Business Analysts & Product Teams – Work effectively with AI-driven Applications
• AI Enthusiasts – Gain practical knowledge in ChatGPT, Prompt Engineering & GenAI
• Chatbot Testing Learners – Learn Playwright Automation for AI Chatbots
• Tech Professionals – Explore Enterprise GenAI Testing, RAGAS & DeepEval Frameworks
• Anyone Interested in AI – Build real-world skills in AI, LLMs & GenAI Testing

 

 

Course syllabus:

🧩 MODULE 1: Introduction to AI Revolution

• Why AI is Everywhere Today
• Evolution of Artificial Intelligence
• Traditional Software vs AI Systems
• Real-World AI Applications Across Industries
• Industry Reality: AI is Powerful but Imperfect
• Need for AI Testing & AI Quality Engineering

---

🧩 MODULE 2: Data & AI Ecosystem

• Data Analytics (Past Insights)
• Data Science (Prediction)
• Artificial Intelligence (Decision Making)
• Relationship Between DA, DS & AI
• AI Ecosystem

---

🧩 MODULE 3: AI Architecture Overview

• Machine Learning (ML)
• Deep Learning (DL)
• Neural Networks (MLP, CNN, RNN)
• Transformers (Core Engine)
• NLP (Language Understanding)
• LLM & GPT
• Generative AI

---

🧩 MODULE 4: Machine Learning Deep Dive

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Real-Time Use Cases
• How AI Models Learn from Data

---

🧩 MODULE 5: Deep Learning & Neural Networks

• Neural Network Architecture
• Input Layer, Hidden Layer & Output Layer
• How Neural Networks Process Information
• CNN (Image Processing)
• RNN (Sequence Data)

---

🧩 MODULE 6: Transformers & NLP Foundations

• Why Transformers?
• Why Transformers Changed AI Forever
• Attention Mechanism
• Encoder–Decoder Architecture
• Tokenization
• Embeddings
• Context Understanding

---

🧩 MODULE 7: Large Language Models (LLMs)

• What is an LLM?
• Tokenization Explained Simply
• Embeddings & Vector Concepts
• How LLM Predicts Next Word
• GPT vs Claude vs Gemini vs perplexity

---

🧩 MODULE 8: Generative AI

• What is Generative AI?
• Text Generation
• Image Generation
• Code Generation
• Limitations of Generative AI
• Hallucination Problems in AI
• Multi-modal AI

---

🧩 MODULE 9: Advanced AI Concepts

• RAG (Retrieval-Augmented Generation)
• AI Agents
• Agentic AI
• MCP (Model Context Protocol)

---

🧩 MODULE 10: Real-World AI Applications

• E-commerce
• Banking
• Healthcare
• Self-driving Cars
• Chatbots (Key Focus)

---

🧩 MODULE 11: Introduction to AI Testing

• Why AI Testing is Needed
• Difference: Traditional vs AI Testing
• Deterministic vs Non-Deterministic Systems
• Why AI Outputs Change Every Time

---

🧩 MODULE 12: Prompt Engineering Basics

• What is Prompt Engineering
• Prompts as the New Programming Language
• Prompt Structure Basics
• Thinking like an AI Tester

---

🧩 MODULE 13: Prompt Testing Fundamental

• Prompt Structure Basics
• Zero Shot Prompting
• Few Shot Prompting
• Chain of Thought Prompting
• Iterative Prompting

---

🧩 MODULE 14: Prompt Engineering Framework

• RACE Framework
  • Role
  • Action
  • Context
  • Expectation
• Writing Effective Prompts

---

🧩 MODULE 15: Prompt Evaluation Metrics

• Instruction Adherence
• Accuracy
• Hallucination
• Groundedness
• Safety testing
• Bias testing
• Prompt Quality

---

🧩 MODULE 16: AI-Based Test Case Generation

• Using Copilot / ChatGPT
• Writing Prompts to generate Test Cases
• Converting User Stories → Test Cases from Azure / Jira
• Sample Automation Format

---

🧩 MODULE 17: Test Case Generator Tool creation and hands on

• Azure Open Ai Key creation
• Test case Generator tool creation through chat gpt / Copilot

---

🧩 MODULE 18: LLM Evaluation (DeepEval Framework) Live Demo

• Problem with LLM Testing
• What is DeepEval?
• Evaluation Flow
• Metrics:
  • Relevance
  • Faithfulness
  • Instruction Adherence
• Custom Metrics

---

🧩 MODULE 19: RAG System Understanding

• Evolution:
  • Keyword Search
  • Semantic Search
  • LLM
  • RAG
• RAG Pipeline (Step-by-Step)

---

🧩 MODULE 20: RAG Challenges & Optimization

• Chunk Size Optimization
• Top-K Retrieval
• Temperature Settings
• Cosine Similarity Concepts
• Common RAG Failure Scenarios
• Hallucination in RAG Systems

---

🧩 MODULE 21: RAG Evaluation (RAGAS Framework) Live Demo

• What is RAGAS?
• Why RAGAS?
• Retrieval vs Generation Evaluation
• Metrics:
  • Faithfulness Validation
  • Context Recall Validation
  • Context Precision Validation
  • Answer Relevance Validation
  • Recall@K & Precision@K

---

🧩 MODULE 22: LLM vs RAG Testing

• Scope Difference
• Failure Types
• Debugging Capability
• Testing Complexity

---

🧩 MODULE 23: Responsible AI & AI Governance

• What is AI Governance?
• Why Governance is Important
• AI Risks & Compliance
• Bias & Fairness
• Explainability
• Privacy & Security
• Human Oversight
• Responsible AI Testing
• AI Safety & Ethical Validation
• Governance in Enterprise AI Systems

---

🧩 MODULE 24: Enterprise GenAI Testing

• What is GenAI Testing?
• Traditional vs GenAI Testing
• Deterministic vs Non-Deterministic Outputs
• Semantic Validation
• Prompt Validation
• Hallucination Testing
• Bias & Toxicity Testing
• Safety Testing
• Context Validation
• Chatbot Testing
• AI Response Evaluation
• LLM Evaluation Frameworks
• Real-Time Enterprise GenAI QA

---

🧩 MODULE 25: Chatbot Testing using playwright

🔹 Introduction to Playwright

• What is Playwright?
• Why Playwright for AI Applications?
• Playwright vs Selenium

🔹 Playwright Basics

• Installation & Project Setup
• Folder Structure
• Running Tests
• Browser Launching
• Locators & Selectors

🔹 Chatbot UI Automation

• Automating Chatbot Interactions
• Sending User Queries
• Capturing AI Responses
• Validating Dynamic Responses
• Automation through Chatgpt, copilot & cursor.

---

🧩 MODULE 26: Final Summary & Industry Perspective

• AI Testing Mindset
• QA Role in AI Systems
• Future of AI Testing
• Question and Answers session
• Interview Questions
• Resume Building