AI & LLM Testing from Beginner to Master – OpenAI, Azure AI Foundry, Prompt Engineering, Metrics, Automation, Performance, CI/CD with GitHub Actions, Grafana Monitoring & Responsible AI
This course is designed to help professionals master AI and Large Language Model (LLM) testing from foundational concepts to advanced, production-ready quality engineering practices. Learners will gain a deep understanding of how AI systems and LLMs behave, why traditional testing approaches fall short, and how to design effective validation strategies for intelligent applications.
The program covers the complete AI testing lifecycle, including AI test planning and strategy, risk-based testing, prompt lifecycle management, and evaluation of AI outputs using meaningful quality metrics such as faithfulness, relevancy, consistency, and safety. Participants will learn to validate AI systems for bias, fairness, hallucinations, toxicity, performance, and usability, ensuring responsible and trustworthy AI behavior.
A strong focus is placed on automation and engineering practices, where learners build AI testing automation using Python, integrate tests into CI/CD pipelines with GitHub Actions, and implement continuous quality gates. The course also introduces production monitoring and observability, leveraging Prometheus and Grafana to track AI quality, detect drift, and respond to issues post-deployment.
By the end of the course, learners will be equipped to take on AI Testing, LLM QA, and AI Quality Engineering roles, confidently contributing to modern AI-driven teams with practical skills, strategic thinking, and industry-relevant experience.
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 – Day1 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 Day2 On:
Indian Timings: 30th January @ 8 PM – 9 PM (IST)/
U.S Timings: 30th January @ 9:30 AM – 10:30 AM (EST)/
U.K Timings: 30th January @ 2:30 PM – 3:30 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 @ 9:30 AM – 10:30 AM (EST)
For Participants in the UK: Monday to Friday @ 2:30 PM – 3:30 PM (BST)
What students have to say about Kavya:
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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?
- Manual testers and QA engineers looking to transition into AI and LLM testing roles
- Automation testers aiming to expand their skill set into AI quality engineering
- QA leads and test managers seeking to understand AI risks, metrics, and testing strategies
- Developers and SDETs involved in building or validating AI-driven applications
- Professionals working on chatbots, GenAI, RAG, or AI-powered products
- Engineering graduates and freshers interested in entering the AI testing and automation domain
- Professionals curious about Responsible AI, model evaluation, and production AI quality
What will I learn by the end of this course?
- Understand how AI and LLM systems work from a testing and quality engineering perspective
- Design AI test strategies and risk-based testing plans for intelligent applications
- Identify and test AI-specific risks such as hallucinations, bias, toxicity, and privacy issues
- Evaluate AI outputs using meaningful quality metrics like faithfulness, relevancy, consistency, and latency
- Manage the prompt lifecycle, including versioning and regression testing
- Build AI testing automation using Python and integrate it into CI/CD pipelines with GitHub Actions
- Monitor AI systems post-deployment using Prometheus and Grafana to detect quality drift
- Apply Responsible AI principles while testing real-world AI applications
- Confidently position yourself for AI Tester, LLM QA, and AI Quality Engineer roles
Course syllabus:
Module 1: Foundations of AI Systems for Quality Engineers
Duration: 3 Hours
AI Systems Overview
- Traditional software vs AI-driven systems
- Rule-based logic vs learning-based behavior
- Deterministic systems vs probabilistic systems
- AI use cases in modern applications
NLP Fundamentals for Testers
- Text as data: tokens and embeddings
- Language understanding vs generation
- Context handling in conversational AI
- Limitations of NLP systems
Large Language Models (LLMs)
- What is a Large Language Model
- High-level LLM architecture concepts
- Pre-training and inference basics
- Model behavior patterns
Prompts & Model Inputs
- Prompt structure and intent
- System, user, and assistant roles
- Context windows and memory
- Tokens, token limits, and truncation
Response Variability
- Temperature and randomness
- Non-deterministic outputs
- Response diversity vs stability
- Repeatability challenges
Testing Implications
- Why exact-output testing fails
- Behavior-based validation mindset
- Managing variability in test results
- Redefining “pass” and “fail” for AI systems
Module 2: Risks, Failure Modes, and Compliance in AI Applications
Duration: 3 Hours
AI Failure Patterns
- Incorrect but confident responses
- Partial answers and omissions
- Overgeneralization and assumptions
- Inconsistent responses across runs
Hallucinations & Accuracy Risks
- Fabricated facts and references
- Lack of source grounding
- Overconfidence in wrong answers
- Sensitivity to prompt phrasing
Bias & Fairness Risks
- Gender and cultural bias
- Stereotyping in responses
- Unequal treatment across user groups
- Representation gaps in training data
Safety & Toxicity
- Harmful or offensive language
- Disallowed or unsafe content
- Refusal vs unsafe compliance
- Context-dependent safety failures
Privacy & Compliance
- Personal data exposure
- Memorization vs generation
- Data leakage risks
- Regulatory awareness (GDPR, AI governance)
Module 3: AI Test Planning and Quality Strategy
Duration: 3 Hours
AI Testing Mindset
- AI systems vs traditional applications
- Uncertainty-driven testing
- Risk-focused quality assurance
Test Strategy for AI
- Defining AI quality goals
- Business risk alignment
- Scope definition for AI features
Risk-Based AI Testing
- High-impact failure identification
- Prioritizing safety and fairness
- Coverage vs cost trade-offs
Test Coverage Decisions
- What to test vs what not to test
- Frequency of AI evaluations
- Token and cost considerations
AI Test Documentation
- AI test strategy artifacts
- Quality criteria and exit conditions
- Stakeholder communication
Module 4: Quality Validation Techniques for AI Outputs
Duration: 4 Hours
Validation Approaches
- Behavior-based validation
- Rule-based checks
- Heuristic evaluation
Functional Validation
- Task completion verification
- Instruction-following checks
- Output structure validation
Content Quality Checks
- Clarity and readability
- Relevance to user intent
- Tone and style consistency
Bias & Safety Validation
- Fairness comparison techniques
- Harmful content detection
- Refusal and safe-completion checks
Performance & UX
- Response latency considerations
- Scalability impact on quality
- Accessibility and inclusivity aspects
Module 5: Measuring and Interpreting AI Output Quality
Duration: 4 Hours
Evaluation Fundamentals
- Why metrics matter in AI testing
- Limitations of pass/fail validation
- Subjective vs objective evaluation
Core Quality Metrics
- Faithfulness to source or context
- Relevancy to user intent
- Completeness of responses
Consistency & Robustness
- Response stability across runs
- Sensitivity to prompt variations
- Edge-case behavior analysis
Safety & Risk Metrics
- Toxicity scoring
- Refusal accuracy
- Harmful content indicators
Scoring Frameworks
- Custom rating scales
- Weighted metric models
- Threshold-based decision making
Quality Trend Analysis
- Regression detection
- Longitudinal quality tracking
- Release readiness indicators
Module 6: Prompt Lifecycle and Test Data Management
Duration: 3 Hours
Prompt as a Test Asset
- Prompts as first-class artifacts
- Prompt ownership and governance
- Prompt reuse and standardization
Prompt Design Principles
- Clear intent definition
- Constraints and instructions
- Ambiguity reduction techniques
Dataset Organization
- Functional prompt collections
- Bias and fairness datasets
- Safety and misuse datasets
Prompt Versioning
- Version control strategies
- Change history and traceability
- Impact assessment workflows
Prompt Regression Testing
- Detecting unintended changes
- Baseline prompt comparisons
- Prompt rollback strategies
Module 7: Programming Foundations for AI Quality Automation
Duration: 5 Hours
Python Basics for Testers
- Python syntax essentials
- Control flow and functions
- Modular script design
Data Handling
- Lists, dictionaries, and mappings
- Parsing JSON and text files
- Structuring test inputs and outputs
API Interaction
- Calling LLM APIs
- Handling request and response data
- Managing configuration values
Error Handling & Logging
- Exception handling patterns
- Retry and timeout logic
- Structured logging for AI tests
Environment Management
- Virtual environments
- Dependency management
- Configuration isolation
Automation Utilities
- Reusable helper functions
- Script maintainability practices
- Code readability standards
Module 8: Automation Frameworks for AI Testing
Duration: 4 Hours
Automation Architecture
- AI test automation layers
- Prompt execution pipelines
- Separation of data, logic, and evaluation
Prompt Execution Automation
- Batch prompt execution
- Response capture mechanisms
- Latency measurement strategies
Validation & Assertions
- Rule-based output checks
- Behavior-based validations
- Keyword and pattern matching
Metric-Driven Automation
- Metric thresholds
- Automated pass/fail criteria
- Quality gate definitions
Handling AI Variability
- Managing non-deterministic outputs
- Flaky test identification
- Stability vs diversity trade-offs
Reporting & Analysis
- Structured test reports
- Metric summaries
- Failure categorization
Module 9: Continuous AI Testing with CI/CD Pipelines
Duration: 3 Hours
CI/CD Fundamentals
- Continuous testing principles
- AI systems in delivery pipelines
- Shift-left quality for AI
Pipeline Integration
- Triggering AI tests on changes
- Code, prompt, and model updates
- Scheduled AI test execution
Quality Gates
- Metric-based release criteria
- Threshold management
- Build pass/fail decisions
Tooling Overview
- GitHub Actions workflows
- Jenkins pipeline concepts
- YAML-based pipeline definitions
Pipeline Challenges
- Flaky AI test handling
- Runtime and cost optimization
- Test result traceability
Module 10: Monitoring, Observability, and Post-Release AI Quality
Duration: 4 Hours
AI Observability Concepts
- Testing vs monitoring
- Pre-production vs production quality
- Continuous quality signals
Metrics & Telemetry
- Latency and throughput metrics
- Error and failure rates
- Safety and refusal indicators
Monitoring Stack
- Prometheus for metrics collection
- Grafana for dashboards
- Visualization of AI quality trends
Drift & Degradation
- Prompt drift detection
- Quality regression signals
- Behavioral anomaly identification
Alerts & Feedback Loops
- Threshold-based alerts
- Incident response basics
- Production feedback into testing
Module 11: Advanced Validation, Case Studies, and Career Alignment
Duration: 4 Hours
Advanced AI Validation
- Fine-tuned model evaluation
- Model comparison strategies
- Controlled behavior verification
Adversarial & Red Team Testing
- Jailbreak prompt concepts
- Policy bypass scenarios
- Misuse and abuse cases
Case Studies
- Real-world AI failures
- Root cause analysis
- Lessons learned from incidents
Responsible AI
- Fairness and accountability principles
- Transparency and trust
- Ethical AI testing practices
Career Alignment
- AI testing roles and responsibilities
- Skill mapping for QA professionals
- Interview preparation focus areas
- Resume and project positioning