AI AUTOMATION
Mastering AI and Large Language Model (LLM)
Testing
From Fundamentals to Production Evaluation.
This course prepares learners to become experts in quality assurance and testing for AI and Large Language Model (LLM) applications. Starting from fundamental AI concepts, learners will master various test types, evaluation metrics, modern tools, and automation frameworks.
Live Interactive Training
Evaluate RAG Pipelines
Build Evaluation Frameworks
Measure Hallucinations
Interview Preparation
Recordings Included
EVAL.PY
from deepeval import assert_test
from deepeval.test_case import LLMTestCase
from deepeval.metrics import AnswerRelevancyMetric
test_case = LLMTestCase(input="...", actual_output="...")
metric = AnswerRelevancyMetric(threshold=0.5)
assert_test(test_case, [metric])
from deepeval.test_case import LLMTestCase
from deepeval.metrics import AnswerRelevancyMetric
test_case = LLMTestCase(input="...", actual_output="...")
metric = AnswerRelevancyMetric(threshold=0.5)
assert_test(test_case, [metric])
1 PASSED 0 FAILED
2.3S
Who this course is for?
Beginners in QA
Software testers interested in AI QA.
QA Professionals
Transitioning to AI/LLM domains.
Devs & Data Scientists
Seeking test automation techniques.
Product Managers
Focused on model reliability and safety.
What you'll become.
After completing this course you'll be able to:
Explain key AI and LLM concepts and architectures
Identify unique challenges in AI/LLM testing
Perform functional, bias, safety, performance, and usability testing for AI systems
Use popular automation frameworks like Playwright, DeepEval, RAGAs, LangSmith, OpenAI Evals, and Promptfoo
Develop automated test suites with comprehensive evaluation metrics
Integrate AI testing into CI/CD workflows for continuous quality assurance
Evaluate AI model outputs for fairness, toxicity, and accuracy
Build trustable, safe, and user-friendly AI applications
Skills you'll master.
FRAMEWORKS
Playwright
DeepEval
RAGAS
LangSmith
OpenAI Evals
Promptfoo
CONCEPTS
RAG Architecture
Hallucinations
Prompt Injection
Bias & Fairness
Toxicity & Safety
Fine-Tuning
Red Teaming
Course curriculum.
Module 0 – Python For AI/LLM Testing
Introduction to Python and Installation Setup
Understanding Variables, Data Types, and Operators
Control Flow: Conditional Statements and Loops
Working with Functions and Reusable Modules
Lists, Tuples, Sets, and Dictionaries in Test Data Handling
String Manipulation and Regular Expressions
File Handling and JSON Data Parsing
Exception Handling, Debugging, and Logging Best Practices
Virtual Environments and Dependency Management Using venv and pip
Writing Clean, Modular, and Maintainable Python Scripts
Mini Hands-On Exercises for Automating Small QA Tasks
Module 1 – Introduction To AI And LLMs
What is Artificial Intelligence?
Evolution and Current Trends in AI
Foundations of Natural Language Processing (NLP)
Introduction to Large Language Models (GPT, PaLM, LLaMA)
Real-World Applications and Case Studies of LLM-Powered Apps
Module 2 – Unique Testing Challenges In AI/LLM
Differences Between Traditional Software and AI Systems Testing
Probabilistic, Non-Deterministic Outputs
Risks: Hallucinations, Bias, Toxicity, Privacy Concerns
Ethical and Legal Implications
Regulatory Landscape and Compliance Basics
Module 3 – Types Of Testing In AI/LLM Applications
Functional Testing Principles in AI Contexts
Bias and Fairness Testing: Detecting and Mitigating Unwanted Bias
Safety and Ethical Testing: Toxicity, Refusal, Harmful Content Prevention
Performance and Scalability Testing for AI Inference Services
Usability and Accessibility Testing for AI User Interfaces
Advanced Types: Explainability, Regression, Localization, Logging/Auditing, Disaster Recovery
Module 4 – Evaluation Metrics For AI/LLM Outputs
Understanding Evaluation Metrics: What to Measure and Why
Core Metrics: Faithfulness, Relevancy, Completeness
Fairness, Bias Scores, Stereotype Detection
Safety Metrics: Toxicity, Refusal Rate
Robustness and Consistency
Latency and Scalability Measures
Sentiment, Readability, Coherence, and Fluency
Privacy and Compliance Metrics
Module 5 – AI/LLM Testing Frameworks And Tools
Overview of Popular Testing Frameworks
Playwright for Frontend UI Automation
DeepEval for Multi-Metric LLM Evaluation
RAGAs for Retrieval-Augmented Generation Pipelines
LangSmith for Monitoring and Evaluation Dashboards
OpenAI Evals for Configurable End-to-End Testing
Promptfoo for Prompt-Output Assertions
Module 6 – Hands-On Setup And Test Automation
Environment Setup: Node.js, Python Virtualenv, API Keys
Writing and Running Playwright UI Tests
Implementing DeepEval Python Test Scripts
Creating RAGAs Evaluation Pipelines
Using LangSmith SDK and Dashboards
Building OpenAI Evals Configs and Running CLI Tests
Defining Promptfoo YAML Test Scenarios
Automating Test Suites for Continuous Integration
Module 7 – Designing Test Suites And Datasets
Curating Effective Test Inputs and Output References
Creating Diverse Prompt Sets for Bias and Safety Analysis
Building Benchmark Datasets for Regression and Model Updates
Incorporating Edge Case and Adversarial Inputs
Dataset Versioning and Maintenance Best Practices
Module 8 – Integrating AI/LLM Testing Into CI/CD Pipelines
Overview of CI/CD Concepts for AI Apps
Connecting Tests to GitHub Actions, Jenkins, or Other Platforms
Automating Evaluations on Model Updates or Frontend Changes
Monitoring Test Results and Alerting Mechanisms
Managing Flaky Tests and False Positives
Module 9 – Fine-Tuning And Red Teaming In AI Systems
Understanding Model Fine-Tuning: Purpose and Benefits
When to Fine-Tune vs. Use Pre-Trained Models
Types of Fine-Tuning: Instruction Tuning, Domain Adaptation, RLHF
Steps in Fine-Tuning: Data Preparation, Labeling, and Evaluation
Testing Fine-Tuned Models for Performance, Bias, and Hallucination Reduction
Continuous Evaluation and Version Tracking After Fine-Tuning
Concept and Importance of Red Teaming in AI Testing
Adversarial Prompt Testing and Jailbreak Scenarios
Safety, Compliance, and Ethical Vulnerability Assessments
Detecting Bias, Toxicity, and Harmful Content Generation
Simulating Attacks for Privacy and Data Leakage
Red Teaming Tools and Frameworks Overview
Integrating Red Team Results into QA Pipelines
Real-World Examples and Lessons Learned from Red Teaming Exercises
Module 10 – Case Studies And Industry Practices
Analysis of Real AI System Failures and Lessons Learned
Successful AI Testing Implementations
Ethical AI and Responsible Innovation in Testing Context
Future Trends: Multimodal AI, Self-Supervised Evaluation
Module 11 – Capstone Project And Assessment
Learners Design and Implement a Full Testing Pipeline
Test Automation Covering UI, Backend, and Model Outputs
Evaluate a Publicly Available LLM or AI Chatbot System
Submit Report and Demo with Lessons Learned
Peer Review and Instructor Feedback