AI & LLM Testing and Automation from Beginner to Master
(Azure AI Foundry, RAG, DeepEval, AI Agents, Generative AI, Prompt Engineering, AI Automation Testing with Playwright, Performance Testing with JMeter, MCP Agents with LangChain & LangFlow, CI/CD using GitHub Actions, Grafana Monitoring)
AI & LLM Testing and Automation from Beginner to Master is a comprehensive, hands-on program designed for software testers, QA engineers, automation testers, SDETs, developers, and working professionals who want to build strong expertise in AI Testing, Large Language Model (LLM) Testing, and AI Test Automation. This practical course covers the complete AI testing lifecycle, including AI and LLM fundamentals, AI testing strategies, prompt testing, test case design, LLM evaluation, AI response validation, Python automation, API testing, RAG testing, Agentic AI testing, and AI quality engineering.
The course introduces modern AI testing and automation techniques to help learners understand AI-driven applications, validate LLM responses, identify hallucinations, assess accuracy and reliability, and automate AI testing workflows using industry-relevant tools and frameworks. Through hands-on exercises, real-time projects, and real-world AI testing scenarios, participants learn to design, execute, automate, and analyze effective tests for AI applications, chatbots, APIs, RAG systems, and intelligent AI agents.
By the end of the course, learners will be proficient in AI Testing, LLM Testing, Generative AI Testing, Prompt Testing, AI Test Case Design, LLM Evaluation, AI Response Validation, Python AI Automation, API Testing, RAG Testing, Agentic AI Testing, AI Security Testing, CI/CD Testing, and AI Quality Engineering. With dedicated hands-on projects, real-world testing experience, advanced automation practices, interview preparation, and expert guidance, this course helps learners develop practical skills for AI/LLM testing and AI quality engineering roles.
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 ✓Highly interactive, hands-on sessions with real-time use cases ✓AI & LLM Testing, prompt validation, and model behavior testing ✓Simplifies complex AI and observability concepts for all levels |
Sample Videos:
AI & LLM Testing and Automation from Beginner to Master– Demo Recording: Live Training
AI & LLM Testing and Automation from Beginner to Master– Day 1 Recording: Live Training
Live Sessions Price:
For LIVE sessions – Offer price after discount is 149 USD 129 109 USD Or USD12000 INR 10900 INR 8900 Rupees.
OR
Free Demo Session On:
7th October @ 9:00 PM – 10:00 PM (IST) (Indian Timings)
7th October @ 11:30 AM – 12:30 PM (EST) (U.S Timings)
7th October @ 4:30 PM – 5:30 PM (BST) (UK Timings)
Class Schedule:
For Participants in India: Monday to Friday @ 9:00 PM – 10:00 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 students have to say about trainer:
| 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 |
What will I Learn by 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
Salient Features:
- 40 Hours of Live Training along with recorded videos
- 1 year access to all recorded sessions
- Course Completion Certificate provided
Who can enroll in 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.
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
- 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 (Updated):
- Custom Python frameworks
- Evaluation scripts
- DeepEval integration
- Reporting utilities
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