AI & LLM Testing and Automation from Beginner to Master
– Live Training
Master the future of Software Testing with our comprehensive AI & LLM Testing and Automation program. This hands-on live training is designed for QA Engineers, Automation Testers, SDETs, Developers, AI Engineers, and professionals who want to build expertise in testing AI-powered applications and Large Language Models (LLMs).
Starting with the fundamentals of Artificial Intelligence (AI), Machine Learning (ML), Generative AI, and Large Language Models (LLMs), you’ll progress to advanced concepts including Prompt Engineering, AI Application Testing, LLM Evaluation, Automation Framework Development, API Testing, RAG Validation, AI Agent Testing, Security Testing, and CI/CD Integration.
Throughout the course, you’ll gain practical experience by working on real-world projects, industry use cases, and automation frameworks using leading AI tools and testing libraries. Learn how to validate AI responses, detect hallucinations, measure model quality, automate regression testing for AI systems, and ensure the reliability, security, and performance of modern AI-powered applications.
About the Instructor:
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Vishnu M is an EX-IITian with 14+ years of extensive industry experience in Performance Testing, Performance Engineering, and AI-Driven Testing. He has worked on complex, large-scale enterprise applications, focusing on system scalability, reliability, optimization, and testing AI/LLM-based systems. His strong foundation in both traditional performance testing and modern AI testing technologies positions him as a trusted expert in next-generation quality engineering. He brings strong hands-on expertise with industry-leading tools such as Apache JMeter, Micro Focus LoadRunner, AppDynamics, and Dynatrace. Vishnu also specializes in AI & LLM Testing, prompt validation, model behavior testing, Chaos Engineering, and advanced performance monitoring and observability. With an unmatched passion for teaching, Vishnu has 14+ years of technical training experience and has trained 700+ students over the last 5 years. His sessions are highly interactive, hands-on, and easy to follow, with a strong focus on real-time use cases and practical exercises. ✓Performance Testing, Performance Engineering, and AI-Driven Testing expertise |
Sample Demo Video:
AI & LLM Testing and Automation from Beginner to Master – Live Training Day 1
Live Sessions Price:
For LIVE sessions – Offer price after discount is 300 USD 259 109 USD Or USD13000 INR 12900 INR 8900 Rupees
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What will I learn by the end of this course?
- Understand core concepts of AI testing and how it differs from traditional software testing
- Validate AI outputs for accuracy, relevance, bias, safety, and hallucinations
- Use evaluation frameworks like DeepEval for automated AI output validation
- Build AI test automation using Python and LLM APIs (OpenAI, Azure OpenAI)
- Monitor AI systems in production using observability tools like Grafana
- Get job-ready for roles in AI testing and AI quality engineering
- Learn how to test AI and LLM-based applications effectively
- Perform prompt testing, prompt regression testing, and prompt version control
- Test Agentic AI systems (multi-step reasoning, tool usage, autonomous flows)
- Handle non-deterministic behavior in AI testing automation
- Detect model drift, performance issues, and quality degradation
- Design AI test strategies and perform risk-based AI testing
- Test RAG (Retrieval-Augmented Generation) pipelines including retrieval quality and grounding validation
- Measure AI quality using metrics like faithfulness, relevancy, and consistency
- Integrate AI testing into CI/CD pipelines using GitHub Actions
- Apply Responsible AI testing practices in real-world projects
What student’s have to say about Vishnu:
| This course made AI and LLM testing very easy to understand. The explanations were simple and practical. I really liked the hands-on sessions and real-time examples.!😊 -Priya S
I had no prior experience in AI testing, but this course helped me learn from scratch. Sessions were interactive, and all my doubts were cleared. – Sagar Dev From the basics to advanced topics, this course covers everything in AI and LLM testing. The interactive sessions and Q&A helped me clear all my doubts. I loved how practical and industry-oriented the training was. Definitely recommend to beginners and experienced testers alike.😊 – Ananya R Simple teaching style, practical approach, and very supportive trainer. This course is perfect for both beginners and working professionals. – Rasool Very informative and enjoyable learning experience! The course content was relevant, up-to-date, and thoughtfully organized. I appreciated the hands-on projects — they really helped solidify my understanding. Excellent for anyone looking to build a career in AI testing. – David This is by far the best AI testing course I’ve taken. The pace was perfect, and every topic was explained with real-time examples that made complex concepts easy to grasp. I now feel confident working on AI testing projects at my job. Worth every minute. – Aditya |
Salient Features:
Who can enroll in this course?
- Software testers and QA professionals who want to learn AI testing
- DevOps engineers looking to integrate AI testing in CI/CD pipelines
- Manual testers planning to move into AI testing and LLM testing
- Data science and ML professionals who want knowledge of AI testing and quality validation
- Automation testers interested in AI test automation using Python
- Fresh graduates interested in starting a career in AI testing
- Professionals who want to upskill in AI testing, prompt testing, and AI quality engineering
- Developers who want to understand AI testing for AI-based applications
Course syllabus:
Module 1: AI Foundations, Risks & Testing Mindset
- Introduction to AI, ML, NLP, Generative AI & LLMs
- Traditional Software Testing vs AI Testing
- Deterministic vs Probabilistic AI Behavior
- LLM Architecture & Response Generation
- Prompt Structure & Context Windows
- AI Failure Patterns & Hallucinations
- Bias, Fairness, Privacy & Responsible AI
- AI Testing Principles & Risk Assessment
- AI Testing Mindset & Best Practices
Module 2: AI Test Strategy & Risk-Based Planning
- AI Test Planning & Strategy
- AI Quality Goals & Acceptance Criteria
- Risk-Based Testing for AI Applications
- AI Failure Scenario Identification
- Test Coverage for AI Features
- Prompt Testing Strategy
- Test Data Planning
- AI Test Artifacts & Documentation
Module 3: AI Output Validation & Quality Metrics
- AI Evaluation Frameworks
- DeepEval Framework
- LLM-as-a-Judge Evaluation
- RAG Output Validation
- Hallucination Detection
- Content Quality Validation
- Bias & Safety Testing
- Performance & Latency Validation
- Faithfulness, Relevancy & Completeness Metrics
- Robustness Testing
- AI Quality Dashboards
Module 4: Prompt Lifecycle & Test Data Management
- Prompt Engineering Fundamentals
- Prompt Versioning & Change Management
- Prompt Regression Testing
- Multi-turn Conversation Testing
- AI Test Dataset Creation
- RAG Test Data Design
- Prompt Reliability & Stability
- Prompt Impact Analysis
- JSON/YAML Test Datasets
- Git-Based Prompt Management
Module 5: Python Foundations for AI Testing Automation
- Python Basics for AI Testers
- JSON & API Handling
- Calling OpenAI & Azure OpenAI APIs
- Exception Handling & Retry Logic
- AI Response Logging
- Automation Utility Development
- DeepEval API Integration
- Structured Evaluation Reports
Module 6: AI Test Automation Framework Development
- AI Automation Framework Architecture
- Prompt Automation
- Rule-Based & Metric-Based Validation
- Handling Non-Deterministic AI Responses
- Automated Test Reporting
- RAG Pipeline Testing
- AI Agent Workflow Testing
- Playwright for AI UI Testing
- Performance & Load Testing of AI APIs
- DeepEval Framework Integration
Module 7: Continuous AI Testing with CI/CD
- AI Testing in CI/CD Pipelines
- GitHub Actions Integration
- Jenkins Integration
- Automated Quality Gates
- Regression Testing for AI Applications
- DeepEval in CI/CD
- Managing Flaky AI Tests
- Cost-Aware AI Test Execution
Module 8: Monitoring, Observability & Production AI Quality
- AI Production Monitoring
- Observability for AI Systems
- AI Telemetry & Metrics
- Prometheus & Grafana Integration
- AI Drift Detection
- RAG Performance Monitoring
- Agent Behavior Monitoring
- Logging & Alerting
- Continuous AI Quality Improvement
Module 9: Advanced AI Validation & Real-Time Projects
- Fine-Tuned Model Validation
- Adversarial & Red-Team Testing
- Agentic AI Testing
- RAG Architecture Testing
- End-to-End AI System Testing
- AI Failure Case Studies
- Responsible AI Testing
- Real-Time Industry Projects
- Interview Preparation & Career Guidance
- Resume Building & Mock Interviews
Tools & Technologies Covered
- OpenAI
- Azure OpenAI
- Azure AI Foundry
- Python
- REST APIs
- DeepEval
- Playwright
- Git & GitHub
- GitHub Actions
- Jenkins
- JSON & YAML
- Prometheus
- Grafana
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- VS Code
Live Sessions Price:
For LIVE sessions – Offer price after discount is 3
00 USD 259109 USD OrUSD13000 INR12900 INR8900 RupeesFAQ
1. What is AI Testing?
AI testing is the process of validating AI and LLM-based applications for accuracy, reliability, safety, bias, and performance.
2. Do I need prior AI knowledge to learn AI testing?
Software testers, QA engineers, automation testers, developers, and freshers interested in AI testing can enroll.
3. Is this AI testing course suitable for beginners?
No. This AI testing course starts from basics and gradually covers advanced AI testing concepts.
4. What tools are used in this AI testing course?
Yes, the course is designed for beginners as well as experienced professionals new to AI testing.
5. Will I learn AI test automation in this course?
You will work with OpenAI, Azure OpenAI, Python, GitHub Actions, Prometheus, and Grafana for AI testing.
6. Does this course cover LLM testing?
Yes, the course covers AI testing automation using Python and real LLM APIs.
7. Will I learn how to test AI for bias and hallucinations?
Yes, this course includes LLM testing, prompt testing, and AI output validation.
8. Is CI/CD covered for AI testing?
Yes, you will learn AI testing techniques to detect bias, hallucinations, and safety issues.
9. What job roles can I apply for after this AI testing course?
Yes, you will learn how to integrate AI testing into CI/CD pipelines using GitHub Actions.
10. What Job roles can I apply for after the AI Testing Course?
After completing this course, you can apply for roles like AI Tester, LLM QA Engineer, AI Quality Engineer, and AI Test Automation Engineer.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.
- VS Code