Generative AI in Performance Testing – Live Training
(Types of language models, prompt engineering, prompting techniques, leveraging AI in different phases)
Isha Training Solutions presents an Extensive and Highly Interactive Course: “Generative AI in Performance Testing” Led With over 12 years of industry experience, the trainer possesses extensive expertise in Artificial Intelligence, Machine Learning, and their practical applications within software testing. Specializing in Generative AI, Large Language Models (LLMs), and prompt engineering, the trainer delivers comprehensive knowledge on AI fundamentals, prompt design, and optimization techniques. This course encompasses the full software testing lifecycle, including AI-enhanced requirement analysis, test planning, test case development, environment configuration, and execution. It also addresses critical topics such as LLM limitations, security risks, and real-world AI applications including chatbots, content generation, and predictive analytics. Participants will gain valuable hands-on experience with performance testing tools like JMeter, augmented by AI capabilities.
The use of Generative AI is becoming increasingly important in every phase of SDLC. It can also be leveraged in the field of performance 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 performance testing.
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 Performance Testing” Demo video
Live Sessions Price:
For LIVE sessions – Offer price after discount is 300 USD 259 89 USD Or USD13000 INR 12900 INR 6900 Rupees
OR
Free Day 1 Session:
19th June @ 8 PM – 9 PM (IST) (Indian Timings)
19th June @ 10:30 AM – 11:30 AM (EST) (U.S Timings)
19th June @ 3:30 PM – 4:30 PM (BST) (UK Timings)
Class Schedule:
For Participants in India: Monday to Friday @ 8 PM – 9 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:
| “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 |
What will I learn by the end of this course?
- Understand the fundamentals of Generative AI, Large Language Models (LLMs), and prompt engineering, including their architecture, parameters, and limitations.
- Apply AI-driven techniques throughout the software testing lifecycle—covering requirement analysis, test planning, test case development, environment setup, and execution.
- Gain hands-on experience using AI-integrated performance testing tools like JMeter to identify bottlenecks, report defects, and optimize test cycles effectively
Salient Features
- 18 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 and QA professionals looking to enhance their skills with AI-driven testing techniques.
- Test automation engineers aiming to integrate Generative AI and Large Language Models into their automation frameworks.
- Performance testers interested in leveraging AI tools like JMeter for smarter test execution and analysis.
- Developers and DevOps engineers seeking to understand AI applications in the software development and testing lifecycle.
- IT professionals and analysts who want to stay updated with emerging AI trends in quality assurance and software testing.
Course syllabus:
Gen AI Fundamentals – (3 Hours)
Introduction, Key concepts and terms
- Overview of Artificial Intelligence (AI) and its significance
- Machine learning
- Deep learning
- Natural language processing
- Generative AI
- Language model
Types of language models
- Large language models
- Small language models
How do LLMs work and their limitations
- Architecture and mechanisms behind LLMs
- Limitations: Bias, hallucinations, and computational cost
What is a prompt
- Definition and role of prompts in AI interactions
Language model parameters
- Explanation of parameters like tokens, context length, and temperature
- Impact of parameter tuning on responses
Security risks
- Data privacy concerns
- Risks of adversarial attacks and misinformation generation
Applications and use cases of AI
- Chatbots, virtual assistants, and customer service
- Code generation, content creation, and predictive analytics
Prompt Engineering – (4 Hours)
What is prompt engineering
- Introduction to prompt engineering
- Definition and significance of crafting effective prompts
Prompt components
- Basic prompt structure
- Prompt frameworks
Formatting and prompt parameters
- Formatting styles
- Temperature, Max tokens and Stop sequences
Prompt tuning process
- Adjusting prompts for specific responses
- Iterative testing and refinement of prompt phrasing
Different prompting techniques
- Shot based prompting
- Sequential prompting
- Context guiding prompting
Best practices
- Best practices in prompt engineering
AI in Software Testing – (11 Hours)
Requirement analysis
- Requirement analysis with AI conversational tools
- Deeper understanding of requirements
- Identify testable requirements
- Requirement traceability matrix
Test planning
- Test strategy and approach preparation
- Selection of different performance testing tools
- Effort estimation
- Risk-based test prioritization
- Identify different types of performance tests
Test case development
- Performance test script development
- Test data creation
Test environment set up
- Test environment creation plan
- Test environment selection
- Verification of test environment
Test execution
- Performance bottlenecks
- Defect reporting
- Daily and weekly status
Test cycle closure
- Assess the test closure cycle
- Test results analysis
- Test metrics preparation
- Test report creation
Jmeter and AI
