Gen AI, AI Agents and MCP in Software Testing (Functional, Automation and Performance Testing) – Live Training

Teacher

Gen AI, AI Agents and MCP in Software Testing (Functional, Automation and Performance Testing) – Live Training

(Types of Language Models, Prompt Engineering & Prompting Techniques, Leveraging AI Across All Phases of Software Testing)

Isha Training Solutions presents an Extensive and Highly Interactive Course: “Gen AI, AI Agents and MCP in Software Testing (Functional, Automation and Performance Testing)” Led by a seasoned industry expert with over 12 years of hands-on experience, this course offers comprehensive, practical insights into Gen AI applications in software testing. With a curriculum designed to align with current job market trends and industry demands, participants will gain in-depth knowledge of Gen AI concepts through real-world examples and hands-on practice.

The use of Generative AI is becoming increasinlgy important in every phase of SDLC. It can also be leveraged in the field of software testing. To harness the potential of this technology in testing, you must learn to interact with different Gen AI tools. This course is designed to make you skilled to use LLMs in different phases of software testing.You will also learn about GitHub Copilot, RAG, AI agents and MCP.

Why Choose This Generative AI, AI agents and MCP in Software Testing Course?

Strong foundation in Generative AI
Hands-on learning with ChatGPT, Gemini, DeepSeek, and GitHub Copilot
Covers advanced topics like RAG (Retrieval-Augmented Generation) and Agentic AI
Practical approach to Prompt Engineering techniques and frameworks
Learn to handle hallucinations, bias, and LLM limitations
Real-time implementation of AI in Software Testing lifecycle (STLC)
Covers requirement analysis, test planning, design, execution, and reporting using AI
Industry-relevant AI skills for functional testing, automation Testing, and QA roles

About the Instructor:

Chandra Kumar has over 12 years of experience in Performance Engineering and Testing. He is a BlazeMeter Certified Apache JMeter and Microfocus LoadRunner Certified Professional. Chandra has extensive expertise with performance testing tools such as Apache JMeter, LoadRunner, and Neoload, as well as APM tools including Dynatrace, AppDynamics, PerfMon, and NMON.He has been providing professional training on Performance Test Tools and Performance Engineering for more than 2 years. In addition, he brings his expertise to “Generative AI in Software Testing: Functional, Automation, and Performance Testing”, offering participants practical insights and hands-on experience in leveraging Generative AI for enhanced software testing.

Sample Videos:

“Generative AI in Software Testing – Functional, Automation and Performance Testing ” -Day1 Video

“Generative AI in Software Testing – Functional, Automation and Performance Testing” -Day2 Video:

Live Sessions Price:

For LIVE sessions – Offer price after discount is 3~~00 USD 259 ~~USD~~~~ 89 USD Or ~~13000 INR~~ ~~12900 INR~~ 6900 Rupees

Enroll For Free Demo

Free Demo On:

Indian Timings: 19th May @ 8:00 PM – 9:00 PM (IST)/

U.S Timings: 19th May @ 10:30 AM – 11:30 AM (EST)/

U.K Timings: 19th May @ 3:30 PM – 4:30 PM (GMT)

Class Schedule:

For Participants in India: Monday to Friday @ 8:00 PM – 9:00 PM (IST)

For Participants in the US: Monday to Friday @10:30 AM – 11:30 AM (EST)

For Participants in the UK: Monday to Friday @ 3:30 PM – 4:30 PM (BST)

What students have to say about Chandra Kumar:

I was good course and instructor is very experienced in the topics explained and cleared all the doubts instantly. – sangeeta

“The Generative AI course was a game-changer! Chandra Sir’s practical examples, along with his deep knowledge, made learning enjoyable. The focus on performance testing with AI tools was particularly enlightening. Thank you for an amazing training experience!”- Arjun Verma

“A transformative learning experience! Chandra Sir’s expertise in Generative AI for software testing opened up new horizons for me. His teaching, combined with hands-on projects, was highly effective. I highly recommend this course to anyone interested in modernizing their testing approach.” – Sneha Reddy

“Excellent session! The detailed coverage of functional, automation, and performance testing using AI tools was exactly what I needed. Chandra Sir’s clear explanations and practical demos provided great insights. I appreciate the real-world case studies and tips shared during the sessions.”- Amit Raj

“Chandra Sir’s teaching style is outstanding! He made advanced AI-driven testing techniques feel simple and accessible. The hands-on exercises were well-structured, and I loved how he answered every question patiently. This training has given me the confidence to explore automation testing with Generative AI.” – Neha Gupta

“The training was insightful and engaging! Chandra Sir explained Generative AI concepts in software testing with real-world examples, making it easy to understand complex topics. His step-by-step approach to automation and performance testing techniques using AI was impressive. Looking forward to applying these skills in my projects!”- Rahul Sharma

First of all thanks for Isha training solutions for giving this wonderful course. Chandra sir has solid experience in Gen AI software testing. He has covered with some practical examples. I have been learned few courses like performance testing, Tosca from Isha training. My journey with Isha training solutions is like around 5 years. All the best everyone who wants to explore new topics. – krishna Mohan

What will I learn by the end of this course?

Basics of Artificial Intelligence, Machine Learning, and Generative AI
Difference between AI vs Generative AI
Understanding Large Language Models (LLMs) and how they work
Working with ChatGPT, Gemini, DeepSeek, and GitHub Copilot
Core concepts of Prompt Engineering and prompt frameworks
Advanced prompting techniques like shot-based, sequential, and context prompting
Handling AI hallucinations, bias, and limitations
Understanding RAG (Retrieval-Augmented Generation) and its architecture
Introduction to Agentic AI and MCP servers
Applying AI in Software Testing lifecycle
Creating test cases, automation scripts, and test data using AI
AI-based test planning, execution, and reporting
Optimizing test coverage and performance testing strategies

Salient Features

25 Hours of On-Demand Live Sessions and Recorded Videos: Gain lifetime access to extensive training materials.
Course Completion Certificate: Receive a certificate upon successful completion of the course.
Hands-On Projects: Engage in real-world projects and live applications to apply the skills learned, ensuring practical, hands-on experience

Who can enroll for this course?

Software Testers (Manual & Automation)
QA Engineers & Test Leads
Developers interested in AI tools and code generation
Freshers looking to start a career in AI + Software Testing
Business Analysts & Product Owners
Anyone interested in Generative AI, LLMs, and Prompt Engineering
Professionals looking to upgrade to AI-driven roles

Course syllabus:

Gen AI Fundamentals – (3 Hours)

1. Introduction, Key concepts and terms:

Overview of Artificial Intelligence (AI) and its significance
Machine learning
Deep learning
Natural language processing
Generative AI
Language model

2. Difference between AI and Gen AI

3. Types of language models:

Large language models
Small language models

4. How do LLMs work

Architecture and mechanisms behind LLMs

5. What is a prompt:

Definition and role of prompts in AI interactions

6. Limitations of LLMs

Cognitive Constraints
Security Risks
Privacy and Legal Concerns

7. Language model parameters

Technical parameters
Behavioral parameters

8. Applications and use cases of AI

Chatbots, virtual assistants, and customer service
Code generation, content creation, and predictive analytics

LLM Examples and Set up – (1 Hours)

9. Introduction to LLMs and their examples

10. Set up of different LLMs – ChatGPT, Gemini, DeepSeek, GitHub Copilot

Prompt Engineering – (4 Hours)

11. What is prompt engineering

Introduction to prompt engineering
Definition and significance of crafting effective prompts

12. Prompt components

Basic prompt structure

13. Prompt Frameworks

14. Formatting and prompt parameters

Formatting styles
Temperature, Max tokens and Stop sequences

15. Prompt tuning process

Adjusting prompts for specific responses
Iterative testing and refinement of prompt phrasing

16. Different prompting techniques

Shot based prompting
Sequential prompting
Context guiding prompting

17. Handling Hallucinations and Biases

18. Best practices

Best practices in prompt engineering

RAG, Agentic AI and MCP servers – (2 Hours)

19. Introduction to RAG (Retrieval-Augmented Generation)

20. How RAG works and its benefits

21. Introduction to Agentic AI

22. How Agentic AI works and its examples

23. Introduction to MCP servers

24. Working with MCP servers

Gen AI in Software Testing – (4 Hours)

25. Requirement analysis

Requirement analysis with AI conversational tools
Deeper understanding of requirements
Identify testable requirements
Requirement traceability matrix

26. Test planning

Test strategy and approach preparation
Selection of different testing tools
Effort estimation
Risk-based test prioritization
Identify different types of performance tests

27. Test case development

Functional test case creation
Automation script development
Performance test script development
Optimizing test coverage and identifying edge cases
Test data creation

28. Test environment set up

Test environment creation plan
Test environment selection
Verification of test environment

29. Test execution

Defect creation
Performance bottlenecks
Defect reporting
Daily and weekly status

30. Test cycle closure

Assess the test closure cycle
Test results analysis
Test metrics preparation
Test report creation

FAQs-Generative AI & Prompt Engineering Course with LLMs, RAG & AI in Software Testing

1. What is Generative AI?

Generative AI is a type of AI that can create content like text, code, images, and more using models like ChatGPT and Gemini.

2. Do I need programming knowledge for this course?

Basic knowledge is helpful, but not mandatory. The course is designed for both beginners and professionals.

3. Which tools will I learn?

You will work with ChatGPT, Gemini, DeepSeek, and GitHub Copilot along with prompt engineering techniques.

4. What is Prompt Engineering?

Prompt Engineering is the skill of designing effective inputs (prompts) to get accurate and useful responses from AI models.

5. What is RAG in AI?

RAG (Retrieval-Augmented Generation) improves AI responses by combining external data with language models.

6. How is this course useful for software testers?

This course teaches how to use AI for test case creation, automation, defect analysis, and performance testing, making testers more productive.

7. Will I get hands-on practice?

Yes, the course includes practical examples, real-time use cases, and tool-based learning.

8. What career opportunities after this course?

You can explore roles like:
AI Test Engineer
Automation Tester with AI
QA Engineer (AI Tools)
Prompt Engineer
AI Analyst

How can I enroll for this course?

Enroll For Free Demo

For any other details, Call me or Whatsapp me on +91 9133190573

Live Sessions Price:

For LIVE sessions – Offer price after discount is ~~129 USD 109 ~~USD~~~~ 89 USD Or ~~15000 INR~~ ~~9900 INR~~ 6900 Rupees.

Sample Course Completion Certificate:

Your course completion certificate looks like this……

Note:

To maintain the quality of our training and ensure a smooth learning experience for all participants, we do not allow batch repetition or switching between courses.

To reiterate, moving from one course to another or shifting from one trainer to another (even if it is the same course) is not possible. Changing batches or trainers in any form is strictly not permitted.

We request all learners to attend the scheduled sessions regularly and make the most of their learning journey. Thank you for your understanding and continued support.

Testimonials:

Course Features

Lectures 65
Quiz 0
Duration 25 hours
Skill level All levels
Language English
Students 0
Assessments Yes

24 Sections
65 Lessons
25 Hours

Expand all sectionsCollapse all sections

Introduction, Key concepts and terms
6
- 1.1
  Overview of Artificial Intelligence (AI) and its significance
- 1.2
  Machine learning
- 1.3
  Deep learning
- 1.4
  Natural language processing
- 1.5
  Generative AI
- 1.6
  Language model
Difference between AI and Gen AI
0
Types of language models:
2
- 3.1
  Large language models
- 3.2
  Small language models
How do LLMs work
1
- 4.1
  Architecture and mechanisms behind LLMs
What is a prompt
1
- 5.1
  Definition and role of prompts in AI interactions
Limitations of LLMs
3
- 6.1
  Cognitive Constraints
- 6.2
  Security Risks
- 6.3
  Privacy and Legal Concerns
Language model parameters
2
- 7.1
  Technical parameters
- 7.2
  Behavioral parameters
Applications and use cases of AI
2
- 8.1
  Chatbots, virtual assistants, and customer service
- 8.2
  Code generation, content creation, and predictive analytics
LLM Examples and Set up
2
- 9.1
  Introduction to LLMs and their examples
- 9.2
  Set up of different LLMs – ChatGPT, Gemini, DeepSeek, GitHub Copilot
Prompt engineering
3
- 10.1
  What is prompt engineering
- 10.2
  Introduction to prompt engineering
- 10.3
  Definition and significance of crafting effective prompts
Prompt components
1
- 11.1
  Basic prompt structure
Prompt Frameworks
0
Formatting and prompt parameters
2
- 13.1
  Formatting styles
- 13.2
  Temperature, Max tokens and Stop sequences
Prompt tuning process
2
- 14.1
  Adjusting prompts for specific responses
- 14.2
  Iterative testing and refinement of prompt phrasing
Different prompting techniques
3
- 15.1
  Shot based prompting
- 15.2
  Sequential prompting
- 15.3
  Context guiding prompting
Best practices
1
- 16.1
  Best practices in prompt engineering
GitHub Copilot and its features
2
- 17.1
  Introduction to GitHub Copilot
- 17.2
  How does it work and its different featues
RAG, Agentic AI and MCP servers
6
- 18.1
  Introduction to RAG (Retrieval-Augmented Generation)
- 18.2
  How RAG works and its benefits
- 18.3
  Introduction to Agentic AI
- 18.4
  How Agentic AI works and its examples
- 18.5
  Introduction to MCP servers
- 18.6
  Working with MCP servers
Gen AI in Software Testing
5
- 19.1
  Requirement analysis
- 19.2
  Requirement analysis with AI conversational tools
- 19.3
  Deeper understanding of requirements
- 19.4
  Identify testable requirements
- 19.5
  Requirement traceability matrix
Test planning
5
- 20.1
  Test strategy and approach preparation
- 20.2
  Selection of different testing tools
- 20.3
  Effort estimation
- 20.4
  Risk-based test prioritization
- 20.5
  Identify different types of performance tests
Test case development
5
- 21.1
  Functional test case creation
- 21.2
  Automation script development
- 21.3
  Performance test script development
- 21.4
  Optimizing test coverage and identifying edge cases
- 21.5
  Test data creation
Test environment set up
3
- 22.1
  Test environment creation plan
- 22.2
  Test environment selection
- 22.3
  Verification of test environment
Test execution
4
- 23.1
  Defect creation
- 23.2
  Performance bottlenecks
- 23.3
  Defect reporting
- 23.4
  Daily and weekly status
Test cycle closure
4
- 24.1
  Assess the test closure cycle
- 24.2
  Test results analysis
- 24.3
  Test metrics preparation
- 24.4
  Test report creation

Chandra Kumar

Chandra Kumar brings over 15 years of experience in Performance Engineering and Testing. He is a Dynatrace Certified Associate Professional, BlazeMeter Certified Apache JMeter and Opentext LoadRunner Certified Professional, with extensive expertise in leading performance testing tools, including Apache JMeter, LoadRunner, and NeoLoad.
Chandra is also highly skilled in Observability and Application Performance Monitoring (APM) tools such as Dynatrace, AppDynamics, Datadog, enabling him to provide deep insights into system performance and optimization.
For over 4 years, he has been delivering professional training in Performance Testing—including JMeter, APM tools, and Performance Engineering—helping participants gain practical, hands-on experience with real-world scenarios.
In addition, Chandra brings his cutting-edge knowledge to the domain of Generative AI in Software Testing, covering Functional, Automation, and Performance Testing. His sessions empower participants to leverage Generative AI for smarter, faster, and more efficient software testing.