AI & LLM Testing for QA Engineers
From Fundamentals to Automated AI Quality Engineering
(Master AI & LLM fundamentals, prompt engineering, LLM evaluation with DeepEval & LLM-as-a-Judge, RAG testing, Python/PyTest automation, AI agent & workflow testing, AI security, and CI/CD quality gates with GitHub Actions)
AI & LLM Testing for QA Engineers – From Fundamentals to Automated AI Quality Engineering is a comprehensive online program designed for software testers, QA engineers, automation testers, SDETs, developers, and working professionals who want to master AI and Large Language Model (LLM) Testing. This hands-on course covers the complete AI testing lifecycle, including AI/ML fundamentals, LLM concepts, prompt engineering, AI test case design, LLM evaluation, functional and non-functional testing, API testing, automated testing, and AI quality engineering practices.
The course introduces modern AI-powered testing techniques to help learners validate LLM applications, evaluate AI responses, identify hallucinations, assess accuracy and reliability, and automate AI testing workflows using industry-relevant tools and frameworks. Through practical exercises, real-time projects, and real-world AI testing scenarios, participants gain the confidence to design, execute, and analyze effective tests for AI-powered applications, chatbots, APIs, and intelligent 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, API Testing, AI Automation Testing, Agent Testing, and AI Quality Engineering. With dedicated hands-on projects, interview preparation, real-world testing experience, and expert guidance, this course prepares learners for AI/LLM testing and quality engineering roles in today’s rapidly evolving software industry.
About The Instructor:
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Passionate AI & LLM Testing professional focused on helping QA Engineers and Automation Professionals build practical skills in testing and evaluating modern AI applications. Tripura brings extensive experience in automation and quality engineering, with a strong focus on Python, PyTest, REST API testing, AI/LLM evaluation, RAG testing, and automated quality engineering. Her training approach connects fundamental testing concepts with the challenges of modern AI systems, including non-deterministic responses, hallucinations, semantic evaluation, safety, and AI quality. With a hands-on and example-driven teaching style, she helps learners understand how to build automated LLM test suites using Python, PyTest and DeepEval, evaluate response relevancy and groundedness, test RAG pipelines and AI agent workflows, and integrate AI quality checks into GitHub Actions CI/CD pipelines. ✓ Building practical AI & LLM testing frameworks using Python and PyTest ✓ Hands-on evaluation of accuracy, relevancy, faithfulness and hallucinations ✓ DeepEval, LLM-as-a-Judge, RAG and AI Agent testing ✓ Integrating automated AI quality gates with GitHub Actions CI/CD
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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:
19th September @ 7:30 PM – 8:30 PM (IST) (Indian Timings)
19th September @ 10:00 AM – 11:00 AM (EST) (U.S Timings)
19th September @ 3:00 PM – 4:00 PM (BST) (UK Timings)
Class Schedule:
For Participants in India: Every Saturday & Sunday @ 7:30 PM – 8:30 PM (IST)
For Participants in the US: Every Saturday & Sunday @10:00 AM – 11:00 AM (EST)
For Participants in the UK: Every Saturday & Sunday@ 3:00 PM – 4:00 PM (BST)
What students have to say about Tripura:
| The way the course builds from AI foundations up to prompt engineering and RAG testing made everything click. The AI security module was the highlight for me –Arjun Sharma
Coming from a manual testing background, the Python and automation sections took time to click, but the hands-on labs made the concepts stick. Tripura explains everything with real project context. –Ayesha Rahman The prompt injection and adversarial testing module was genuinely useful — seeing how to validate AI agents and secure LLM applications gave me a real edge in interviews. –Daniel Thomas I switched from a functional testing role and was worried about keeping up, but the daily hands-on labs on prompt validation and RAG testing built my confidence step by step. – Sneha Reddy The live sessions on LLM evaluation and hallucination detection were exactly what I needed for my current job. Doubt-clearing over WhatsApp between classes was a big help too. –Michael Anderson Solid, structured curriculum — RAG and LLM evaluation concepts were explained with real testing examples instead of just slides. The certificate helped me negotiate a better offer. –Mary Joseph |
What will I Learn by end of this course?
- AI, Generative AI & LLM fundamentals
- Tokens, context windows & LLM parameters
- LLM application architecture & RAG/agent workflows
- Embeddings, semantic similarity & vector databases
- Prompt engineering & prompt test design
- Testing prompt robustness & structured outputs
- Hallucination detection & factual accuracy verification
- Bias, toxicity, fairness & data privacy testing
- LLM-as-a-Judge techniques & evaluation rubrics
- Python essentials & PyTest for LLM test automation
- REST APIs, JSON handling & secrets management
- Automated evaluation with DeepEval
- RAG chunking, retrieval & response validation
- RAG regression & end-to-end pipeline testing
- AI agent & multi-step workflow testing
- Prompt injection, jailbreak & AI security testing
- Chatbot UI & non-deterministic response testing
- CI/CD integration & AI quality gates with GitHub Actions
- Production monitoring, observability & drift tracking
Salient Features:
- 35 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?
- Whether you’re starting your career or upskilling your existing QA expertise, this course is designed to help you become a confident AI Testing & Quality Engineering professional.
- QA Engineers — Transition into AI & LLM Testing
- SDETs & Automation Testers — Add AI/LLM testing skills
- Software Engineers & Developers — Test and validate AI-powered applications
- IT Professionals — Upskill for emerging AI Quality Engineering roles
- Freshers & Beginners — Build a foundation in AI and LLM Testing
- Security Testers — Explore AI Security, Prompt Injection & LLM Security Testing
- Performance & API Testers — Learn AI application and LLM API testing
- AI/ML Professionals — Strengthen AI quality and evaluation skills
- Testing Professionals — Explore Generative AI, RAG, LLM & Agent Testing
Course syllabus:
Module 1: AI, Generative AI & LLM Fundamentals
- AI & QA Role
- AI, ML, DL & Generative AI
- Introduction to LLMs
- Tokens & Context Windows
- LLM Parameters
- Working with LLM APIs
- Traditional Testing vs. AI Testing
Module 2: LLM Application Architecture, RAG & Agentic Workflows
- Anatomy of an LLM Application
- Prompt Architecture
- LLM Application Workflows
- Function & Tool Calling
- Embeddings & Semantic Similarity
- Vector Databases & Retrieval
- RAG Architecture
- AI Agents & Agentic Workflows
Module 3: Fundamentals of AI & LLM Testing
- Why AI Testing Is Different
- Types of LLM Testing
- Accuracy & Factual Testing
- Answer Relevancy
- Hallucination Detection
- Bias, Toxicity & Fairness
- Privacy & Data Leakage
- Core AI Quality Dimensions
Module 4: Prompt Engineering & Prompt Test Design
- Prompt Engineering Basics
- Common Prompt Patterns
- Prompt Test Design
- Prompt Robustness Testing
- Structured Output Testing
- Prompt Versioning
- LLM Test Dataset
Module 5: LLM Evaluation & LLM-as-a-Judge
- The LLM Evaluation Problem
- Reference-Based vs. Reference-Free Evaluation
- LLM-as-a-Judge Techniques
- Evaluation Rubrics
- Semantic Similarity & Thresholds
- Evaluator Bias & Consistency
- Custom AI Evaluator & Lab
Module 6: Python & PyTest for LLM Test Automation
- Python Environment Setup
- Python Essentials for AI Testing
- Working with REST APIs
- Secrets Management
- PyTest Fundamentals
- Automated LLM Test Cases
- Data-Driven Testing
- Modular Test Framework
Module 7: Automated Evaluation with DeepEval
- Introduction to DeepEval
- Structuring Test Cases
- Answer Relevancy Evaluation
- Faithfulness & Groundedness
- Hallucination Metrics
- Custom Metrics & Judge Prompts
- Multi-Turn Conversation Testing
- Reports & Hands-On Lab
Module 8: RAG Application & Retrieval Testing
- RAG Test Strategy
- Ingestion & Chunking Tests
- Embedding & Vector DB Validation
- Precision, Recall & Relevancy
- End-to-End RAG Suite
- RAG Regression Testing
Module 9: AI Agent & Workflow Testing
- AI Agent Testing Strategies
- Function & Tool Calling Validation
- Multi-Step Workflow Testing
- Agent Memory & Context Retention
- Failure & Recovery Testing
Module 10: AI Security, Safety & Robustness
- AI Security Testing Fundamentals
- Prompt Injection
- Basic Jailbreak Scenarios
- PII & System Prompt Leakage
- Toxicity, Harm & Bias Scenarios
- Input Robustness
Module 11: Chatbot UI & Non-Deterministic Testing
- AI Application Interfaces
- Manual & Automated Chatbot Testing
- Non-Deterministic UI Responses
- Simulating User Conversations
- Timeouts & Streaming Responses
- API + Frontend Validation
Module 12: Continuous AI Integration & CI/CD Pipelines
- AI Tests in Continuous Integration
- Test Data Management
- Automated Reporting & Thresholds
- PyTest & DeepEval in GitHub Actions
- Managing Secrets Securely
- AI Quality Gates
- Costs & Flakiness Management
Module 13: Production Quality, Monitoring & Capstone
- Pre-Production vs. Production
- Observability Concepts
- Tracking Drift & Degradation
- Capstone Project