AI & LLM Testing and Automation Course with Generative AI for QA Engineers – Live Training
(Python Foundations, AI & LLM Concepts, Testing Strategies, Automation Frameworks, Evaluation Metrics, CI/CD Integration, Red Teaming, and Real-World Case Studies)
This AI & LLM Testing and Automation with Generative AI course is designed to equip QA Engineers and SDETs with the skills required to test modern AI-driven applications. The program covers Python foundations, AI and Large Language Model concepts, and the unique challenges of testing probabilistic systems. Learners gain hands-on experience with industry-standard tools such as Playwright, DeepEval, RAGAs, LangSmith, and Promptfoo. The course emphasizes evaluation metrics, bias and safety testing, prompt validation, and CI/CD integration for AI systems. Through real-world case studies and a capstone project, participants learn to design, automate, and operationalize enterprise-grade AI testing frameworks.
Why Learn This Course?
AI and Generative AI systems behave differently from traditional software, making conventional testing approaches insufficient. This course helps you master AI and LLM testing methodologies to detect hallucinations, bias, safety risks, and model drift before they impact users. You will gain hands-on experience with industry-standard AI testing and evaluation tools used in real-world projects. The program enables you to automate AI testing pipelines, integrate testing into CI/CD workflows, and ensure responsible, compliant AI deployments. By completing this course, you position yourself as a future-ready QA professional with in-demand AI testing and Generative AI automation skills.
About The Instructor:
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Takshin Varma – AI & Automation Testing Expert Takshin Varma is an experienced AI-driven testing and automation professional with 8 years of industry experience in modern QA engineering. He specializes in AI and LLM testing, Python-based automation, prompt engineering, bias and fairness validation, and CI/CD-integrated test frameworks. With strong exposure to tools and practices aligned to real-world enterprise systems, Takshin brings practical insight into validating intelligent and generative AI applications. As a trainer, Takshin has over 3 years of teaching experience and has successfully trained 200+ students across QA, automation, and AI testing domains. His teaching style is simple, structured, and hands-on, focusing on real-time use cases and project-based learning. He is committed to helping learners gain job-ready skills and confidently transition into AI-enabled testing roles. |
Live Sessions Price:
For LIVE sessions – Offer price after discount is 200 USD 159 119 USD Or USD15000 INR 12000 INR 8900 Rupees.
OR
Free Day 3 Session:
16th February @ 9:00 PM – 10:00 PM (IST) (Indian Timings)
16th February @ 10:30 AM – 11:30 AM (EST) (U.S Timings)
16th February @ 3:30 PM – 4:30 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 @ 10:30 AM – 11:30 AM (EST)
For Participants in the UK: Monday to Friday @ 3:30 PM – 4:30 PM (BST)
What will I Learn by end of this course?
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Salient Features:
- 40+ Hours of Live Training along with recorded videos
- Lifetime access to all recorded sessions
- Course Completion Certificate provided
Who can enroll in this course?
- QA Engineers and Software Testers who want to upskill in AI testing, LLM testing, and Generative AI validation
- SDETs and Automation Engineers looking to extend their expertise into AI and LLM test automation frameworks
- AI Engineers and Data Scientists who need structured approaches for model evaluation, bias testing, and quality assurance
- Product Engineers and Developers working on AI-powered applications, chatbots, copilots, and RAG systems
- DevOps and MLOps professionals involved in CI/CD pipelines, monitoring, and production AI deployments
- Technology teams and enterprises seeking corporate training in AI and LLM testing best practices
- Professionals with basic Python knowledge who want to transition into AI and Generative AI testing roles
Course syllabus:
Module 0: Introduction to AI and Large Language Models
🎯 Learning Objectives
- Understand how AI and LLMs work internally
- Identify where testing differs from traditional systems
📘 Topics Covered
- What is Artificial Intelligence?
- Evolution: Rule-based → ML → Deep Learning → LLMs
- NLP fundamentals (tokenization, embeddings, transformers)
- Overview of LLMs: GPT, PaLM, LLaMA
- Prompt–completion lifecycle
- Real-world LLM applications (chatbots, copilots, RAG)
🧪 Hands-On Labs
- Interact with an LLM via API
- Observe non-deterministic outputs
- Compare responses for same prompt
✅ Outcome
Learners understand what they are testing and why it behaves differently.
Module 1: Python Foundations for AI Test Automation
🎯 Learning Objectives
- Build strong Python fundamentals required for AI/LLM testing
- Write clean, modular test scripts
- Handle real-world test data formats and logs
📘 Topics Covered
- Python installation and IDE setup (VS Code, PyCharm)
- Variables, data types, operators
- Control flow: if , for , while
- Functions, modules, and reusable utilities
- Data structures: lists, tuples, sets, dictionaries
- String manipulation & regex for prompt/output validation
- File handling (TXT, CSV, JSON)
- Exception handling & debugging techniques
- Logging best practices for AI test pipelines
- Virtual environments ( venv , pip , requirements.txt )
- Writing maintainable test utilities
🧪 Hands-On Labs
- Write a Python script to validate LLM responses
- Regex-based hallucination keyword detection
- JSON parsing for prompt–response datasets
- Build a reusable test helper library
✅ Outcome
Learners can confidently write Python-based AI test scripts.
Module 2: Unique Testing Challenges in AI/LLM Systems
🎯 Learning Objectives
- Identify risks unique to AI systems
- Design tests for probabilistic behavior
📘 Topics Covered
- Deterministic vs probabilistic systems
- Hallucinations and overconfidence
- Bias, toxicity, and fairness risks
- Data privacy and leakage concerns
- Legal & regulatory considerations (GDPR, AI Act basics)
- Model drift and prompt sensitivity
🧪 Hands-On Labs
- Create prompts that expose hallucinations
- Bias testing using demographic variations
- Toxicity detection experiments
✅ Outcome
Learners can anticipate AI-specific failures and risks.
Module 3: Types of Testing in AI/LLM Applications
🎯 Learning Objectives
- Apply classical testing concepts to AI systems
- Expand QA beyond functional correctness
📘 Topics Covered
- Functional testing for AI features
- Bias & fairness testing
- Safety & ethical testing
- Performance & scalability testing
- Usability & accessibility testing
- Advanced testing:
- Explainability testing
- Regression testing for prompts/models
- Localization testing
- Logging & auditing
- Disaster recovery scenarios
🧪 Hands-On Labs
- Create test cases for unsafe prompt handling
- Regression tests for prompt updates
- Latency benchmarking
✅ Outcome
Learners design multi-dimensional AI test strategies.
Module 4: Evaluation Metrics for AI/LLM Outputs
🎯 Learning Objectives
- Measure AI quality objectively
- Select the right metrics for each use case
📘 Topics Covered
- Why evaluation metrics matter
- Faithfulness, relevance, completeness
- Bias & fairness metrics
- Toxicity and refusal rates
- Robustness & consistency
- Latency, throughput, cost metrics
- Readability, coherence, fluency
- Privacy & compliance indicators
🧪 Hands-On Labs
- Score LLM outputs using predefined metrics
- Compare human vs automated evaluation
- Track metric drift over time
✅ Outcome
Learners can quantify AI quality , not just observe it.
Module 5: AI/LLM Testing Frameworks and Tools
🎯 Learning Objectives
- Understand the AI testing ecosystem
- Choose the right tool for the job
📘 Topics Covered
- Overview of AI testing frameworks
- Playwright for AI UI testing
- DeepEval for LLM evaluation
- RAGAs for RAG pipeline testing
- LangSmith for monitoring & tracing
- OpenAI Evals for end-to-end testing
- Promptfoo for prompt assertions
🧪 Hands-On Labs
- Write Playwright tests for AI UI
- Create DeepEval test cases
- Promptfoo YAML assertions
✅ Outcome
Learners gain tooling confidence used in industry.
Module 6: Hands-On Setup and Test Automation
🎯 Learning Objectives
- Build real, automated AI test pipelines
📘 Topics Covered
- Environment setup (Node.js, Python, API keys)
- UI automation with Playwright
- DeepEval test scripts
- RAGAs pipeline evaluation
- LangSmith dashboards
- OpenAI Evals CLI
- Promptfoo automation
- CI-friendly test execution
🧪 Hands-On Labs
- End-to-end automated AI test suite
- Run tests locally and via CI
✅ Outcome
Learners can automate AI testing at scale .
Module 7: Designing Test Suites and Datasets
🎯 Learning Objectives
- Build robust datasets for AI evaluation
📘 Topics Covered
- Prompt dataset design principles
- Bias & safety prompt sets
- Benchmark dataset creation
- Adversarial and edge-case prompts
- Dataset versioning & maintenance
🧪 Hands-On Labs
- Create a regression dataset
- Design adversarial prompts
✅ Outcome
Learners design high-quality AI test data .
Module 8: Integrating AI Testing into CI/CD Pipelines
🎯 Learning Objectives
- Make AI testing continuous and reliable
📘 Topics Covered
- CI/CD concepts for AI apps
- GitHub Actions / Jenkins integration
- Automated testing on model updates
- Monitoring and alerting
- Handling flaky AI tests
🧪 Hands-On Labs
- GitHub Actions pipeline for AI tests
- Failure alerts setup
✅ Outcome
Learners operationalize AI testing in DevOps.
Module 9: Fine-Tuning and Red Teaming
🎯 Learning Objectives
- Test models beyond default safety
- Identify and mitigate vulnerabilities
📘 Topics Covered
- Fine-tuning concepts and workflows
- Instruction tuning & domain adaptation
- Testing fine-tuned models
- Red teaming fundamentals
- Jailbreak & adversarial testing
- Privacy & data leakage simulations
- Red team tools and frameworks
- Feeding results back into QA pipelines
🧪 Hands-On Labs
- Jailbreak prompt testing
- Red team test reports
✅ Outcome
Learners can break AI systems before attackers do .
Module 10: Case Studies and Industry Practices
🎯 Learning Objectives
- Learn from real-world failures and successes
📘 Topics Covered
- Famous AI failures and root causes
- Enterprise AI testing strategies
- Responsible AI practices
- Future trends: ○ Multimodal testing
- Self-evaluating AI systems
🧪 Hands-On Labs
● Case study analysis & presentation
✅ Outcome
Learners think like AI quality leaders , not just testers.
Module 11: Capstone Project
🎯 Capstone Requirements
- Design a full AI testing pipeline
- UI + backend + model evaluation
- Evaluate a public AI system
- Final report + demo
- Peer & instructor review