AI & LLM Testing and Automation from Beginner to Master – OpenAI, Azure AI Foundry, RAG, DeepEval, AI Agents, Generative AI, Prompt Engineering, AI Automation Testing with Playwright, Performance Testing with JMeter, CI/CD using GitHub Actions, Grafana Monitoring – Live Training
This AI testing course is a comprehensive, hands-on program designed to help software testers, QA engineers, and automation professionals transition into AI testing and LLM quality engineering. Covering everything from AI fundamentals to advanced validation and production monitoring, the course equips learners with real-world skills required to test modern AI-driven systems confidently.
Unlike traditional software testing, AI testing requires validating probabilistic behavior, managing uncertainty, and measuring output quality instead of fixed expected results. This course begins by building a strong AI testing mindset, explaining how Large Language Models (LLMs) work, why conventional testing approaches fail, and how to identify AI-specific risks such as hallucinations, bias, safety issues, and compliance concerns.
You will learn how to design AI test strategies, perform risk-based AI testing, define quality goals, and validate AI outputs using metrics like faithfulness, relevancy, completeness, toxicity, and response stability. The curriculum places heavy emphasis on practical AI testing techniques, including prompt validation, prompt regression testing, and managing prompts as first-class test assets.
The course also provides deep hands-on exposure to AI test automation using Python, where you’ll build custom automation frameworks to execute prompts, evaluate LLM responses, handle non-deterministic behavior, and generate structured test reports. You’ll integrate AI testing into CI/CD pipelines using GitHub Actions and learn how to create automated quality gates for AI releases.
In advanced modules, you’ll explore continuous AI testing, production monitoring, and observability using Prometheus and Grafana. You’ll learn how to detect model drift, performance degradation, and safety issues after deployment—critical skills for real-world AI testing in enterprise environments.
By the end of this AI testing certification course, you’ll be job-ready for roles such as AI Tester, LLM QA Engineer, AI Quality Engineer, or AI Test Automation Engineer, with strong interview preparation and real project exposure aligned to industry expectations.
Why Learn This AI Testing Course?
- AI testing is a fast-growing and in-demand skill
- Learn how to test AI and LLM applications correctly
- Move beyond traditional testing into AI testing
- Get hands-on experience with real AI testing tools
- Learn AI test automation using Python
- Understand AI issues like bias, hallucinations, and safety
- Add AI testing skills to your resume
- Improve job and career opportunities in AI testing
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. His ability to combine performance engineering, AI testing, and resilience testing helps learners understand how to test modern, intelligent, and highly scalable systems. 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. He has a natural talent for simplifying complex performance, AI, and observability concepts, making his training highly effective for both beginners and experienced professionals. |
Sample Videos:
AI & LLM Testing from Beginner to Master Live Training – Demo Recording
AI & LLM Testing from Beginner to Master Live Training – Day 1 Recording
Live Sessions Price:
For LIVE sessions – Offer price after discount is 129 USD 119 109 USD Or USD15000 INR 12900 INR 8900 Rupees.
OR
Free Demo On:
Indian Timings: 8th April @ 8 PM – 9 PM (IST)/
U.S Timings: 8th April @ 10:30 AM – 11:30 AM (EDT)/
U.K Timings: 8th April @ 3:30 PM – 4:30 PM (BST)
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 (EDT)
For Participants in the UK: Monday to Friday @ 3:30 PM – 4:30 PM (BST)
What students have to say about Vishnu:
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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
- 40 Hours of Instructor-Led Live Training with practical, industry-focused sessions
- Session recordings provided for revision and self-paced learning
- Hands-on, real-world oriented curriculum focused on modern AI systems
- Coverage of OpenAI, Azure AI Foundry, CI/CD, Monitoring, and Responsible AI
- Practical automation exposure using Python and modern testing approaches
- Capstone-style learning approach with real AI testing scenarios
- Course Completion Certificate upon successful completion
Who can enroll for this course?
- Software testers and QA professionals who want to learn AI testing
- Manual testers planning to move into AI testing and LLM testing
- Automation testers interested in AI test automation using Python
- Developers who want to understand AI testing for AI-based applications
- DevOps engineers looking to integrate AI testing in CI/CD pipelines
- Data science and ML professionals who want knowledge of AI testing and quality validation
- Fresh graduates interested in starting a career in AI testing
- Professionals who want to upskill in AI testing, prompt testing, and AI quality engineering
What will I learn by the end of this course?
- Understand core concepts of AI testing and how it differs from traditional software testing
- Learn how to test AI and LLM-based applications effectively
- Design AI test strategies and perform risk-based AI testing
- Validate AI outputs for accuracy, relevance, bias, safety, and hallucinations
- Perform prompt testing, prompt regression testing, and prompt version control
- Test RAG (Retrieval-Augmented Generation) pipelines including retrieval quality and grounding validation
- Use evaluation frameworks like DeepEval for automated AI output validation
- Test Agentic AI systems (multi-step reasoning, tool usage, autonomous flows)
- Measure AI quality using metrics like faithfulness, relevancy, and consistency
- Build AI test automation using Python and LLM APIs (OpenAI, Azure OpenAI)
- Handle non-deterministic behavior in AI testing automation
- Integrate AI testing into CI/CD pipelines using GitHub Actions
- Monitor AI systems in production using observability tools like Grafana
- Detect model drift, performance issues, and quality degradation
- Apply Responsible AI testing practices in real-world projects
- Get job-ready for roles in AI testing and AI quality engineering
Course syllabus:
Module 1: AI Foundations, Risks, and Testing Mindset
Duration: 5 Hours
- Traditional software vs AI-driven systems
- Deterministic vs probabilistic behavior in AI
- NLP basics for testers (tokens, context, embeddings)
- How LLMs generate responses (OpenAI, Azure OpenAI)
- Prompt structure, context windows, and response variability
- Why expected-output testing fails for AI systems
- Common AI failure patterns and hallucinations
- Bias, fairness, safety, and privacy risks in LLM outputs
- Regulatory awareness and Responsible AI fundamentals
- AI testing mindset and uncertainty-driven testing approaches
Tools / Platforms: OpenAI, Azure AI Foundry, Azure OpenAI Playground
Module 2: AI Test Strategy and Risk-Based Planning
Duration: 3 Hours
- Differences between traditional and AI-focused test planning
- Defining AI quality goals and acceptance criteria
- Risk-based testing strategies for LLM applications
- Identifying high-impact AI failure scenarios
- Test scope and coverage decisions for AI features
- Cost-aware testing and token usage considerations
- AI test documentation and stakeholder communication
- Risk modeling for RAG systems (retrieval failure, hallucinated grounding)
- Agentic AI risk identification (looping, tool misuse, goal deviation)
Tools / Artifacts: AI test strategy templates, risk matrices, prompt catalogs
Module 3: AI Output Validation and Quality Metrics
Duration: 5 Hours
- Introduction to AI evaluation frameworks (DeepEval, prompt-based evaluators)
- Using DeepEval for automated evaluation (faithfulness, answer relevancy, context precision)
- LLM-as-a-judge evaluation techniques
- Groundedness validation for RAG outputs
- Evaluating hallucinations vs factual correctness
- Dataset-based vs dynamic evaluation approaches
- Behavior-based vs rule-based validation techniques
- Task completion and instruction-following checks
- Content quality validation (clarity, relevance, tone)
- Bias, fairness, and safety validation methods
- Performance validation (latency, consistency, response stability)
- Faithfulness, relevancy, and completeness metrics
- Robustness across prompt variations
- Toxicity, refusal, and safety-related metrics
- Custom scoring models and quality thresholds
- Release readiness assessment and quality trend analysis
- Tools / Techniques (Updated):
- Prompt-based validation
- Metric scoring models
- DeepEval framework
- Evaluation dashboards
Tools / Techniques: Prompt-based validation, Metric scoring models,DeepEval framework,Evaluation dashboards
Module 4: Prompt Lifecycle and Test Data Management
Duration: 3 Hours
- RAG test dataset creation (query + expected context + expected answer)
- Agent workflow prompt chaining and testing
- Test dataset design for multi-turn conversations and agents
- Prompts as first-class test assets
- Prompt design principles for reliability and testability
- Functional, bias, and safety prompt datasets
- Prompt versioning and change management
- Prompt regression testing strategies
- Impact analysis for prompt and model updates
Tools / Practices: JSON/YAML prompt datasets, GitHub version control
Module 5: Python Foundations for AI Testing Automation
Duration: 4 Hours
- Python essentials for AI testers
- Data structures for test inputs and outputs
- Reading and writing JSON and text data
- Calling LLM APIs (OpenAI, Azure OpenAI) and handling responses
- Exception handling, retries, and timeout logic
- Logging AI responses, errors, and latency
- Writing clean, maintainable automation utilities
- Calling evaluation frameworks (DeepEval APIs / libraries)
- Handling structured evaluation outputs (scores, reasoning)
Tools / Languages: Python, REST APIs, VS Code
Module 6: AI Test Automation Frameworks and Execution
Duration: 4 Hours
- Architecture of AI test automation systems
- Automating prompt execution and evaluations
- Rule-based and metric-driven validations
- Handling non-deterministic and flaky AI tests
- Defining automated quality gates
- Generating structured test reports and summaries
- Integrating DeepEval into automation frameworks
- Automating RAG pipeline testing (retrieval + generation validation)
- Testing multi-step Agentic AI workflows
- Simulating user journeys for AI agents
- Validating tool usage and intermediate reasoning steps
- Tools / Approaches (Updated):
- Custom Python frameworks
- Evaluation scripts
- DeepEval integration
- Reporting utilities
- Performance/load testing of AI APIs
- Testing LLM endpoints under concurrent users
- Measuring latency under load
🔹 Playwright for End-to-End AI Testing of Ui based AI apps.
Tools / Approaches: Custom Python frameworks, Evaluation scripts, Reporting utilities, DeepEval integration
Module 7: Continuous AI Testing with CI/CD Pipelines
Duration: 3 Hours
- Continuous testing concepts for AI systems
- Integrating AI tests into CI/CD pipelines
- Triggering tests on code, prompt, or model changes
- Quality gates using evaluation metrics
- GitHub Actions workflows for AI testing
- Jenkins overview (conceptual exposure)
- Managing flaky tests and execution costs
- Running DeepEval tests in CI/CD pipelines
- Automated quality gates based on evaluation scores
- Regression testing for RAG and agent workflows
Tools: GitHub Actions, Jenkins (conceptual)
Module 8: Monitoring, Observability, and Production AI Quality
Duration: 3 Hours
- Pre-release testing vs post-release monitoring
- Observability concepts for AI systems
- Latency, failure, and safety telemetry
- Metrics collection using Prometheus
- AI quality dashboards using Grafana
- Detecting drift, degradation, and anomalies
- Alerts, feedback loops, and continuous improvement
- Monitoring RAG pipeline performance (retrieval accuracy, latency)
- Tracking agent behavior in production (failures, loops, incorrect actions)
- Observability for agentic workflows
- Logging intermediate steps in AI agents
Tools: Prometheus, Grafana
Module 9: Advanced Validation, Case Studies, and Career Alignment
Duration: 2 Hours
- Evaluating fine-tuned and customized LLM models
- Adversarial and red-team testing concepts
- Analysis of real-world AI failures
- Responsible AI practices in testing
- End-to-end AI quality engineering workflows
- AI testing roles and career paths
- Interview preparation and resume positioning
- RAG architecture testing (embeddings, vector DB, retrieval failures)
- Agentic AI testing strategies and challenges
- Failure case studies: RAG hallucinations, agent misbehavior
- End-to-end testing of AI systems (RAG + Agents + APIs)
Focus Areas: Real project scenarios, Career alignment, Agentic AI validation, RAG systems testing