LLM Testing Masterclass: Software QA for AI Language Models

Name: LLM Testing Masterclass: Software QA for AI Language Models
Rating: 4.2 (52 reviews)

Learn Professional AI Testing, LLM Validation, and RAG Application Testing from Scratch

Created byImran Ali

Last updated 10/2025

English

What you'll learn

LLM Testing Fundamentals
Functional testing (content processing, logical consistency)
Non-functional testing (robustness, performance optimization)
DeepEval Framework
RAG Testing
Hallucination Detection
Python AI Testing
Integrating Vector Databases

Course content

5 sections • 39 lectures • 5h 29m total length

Course Overview1:36
Demo of Application Under Test - Shoe Store RAG ChatBot5:31
Basics of LLM10:18
Testing Challenges of LLM7:38
Download Ollama and Tinyllama Model3:09
LLM Testing Categories And Strategy - Part 111:11
LLM Testing Cateogies And Strategy - Part 26:58
Brief Survey Of LLM Testing Tools4:13
Surveying tooling for LLM testing, this lecture covers evaluation frameworks, benchmarks, security, prompt testing, integration, and performance, and maps these tools to functional and security testing categories.
(New) Benchmarks vs Testing/Evaluation Frameworks4:12
Interact With Ollama REST API Using Postman6:17
Interact With Ollama REST API Using Postman - Part 23:01

Writing Our Own Python REST Client13:55
Overview Of All Tests Written For This Course5:30
Functional Testing - Basic Functionality - Part 16:29
Use a pytest fixture to initialize a test client for the tiny llama model, skip if unavailable, and perform a functionality test that calls generate and checks a non-empty response.
Functional Testing - Basic Functionality - Part 219:00
Functional Testing - Content Processing And Generation15:58
Functional Test - Context Learning - Part 113:42
Functional Test - Context Learning - Part 210:21
Perform a functional test of context learning by evaluating a language model’s error handling through command-line prompts, and write assertions to detect nonsensical or undefined responses.
Non-Functional Test - Performance11:11
Non-Functional Test - Robustness6:22
Non-Functional Test - Hallucination Detection11:46

(New) LLM As A Judge5:57
Introduction To DeepEval4:20
Writing Our OpenAI Client Wrapper17:59
Build an OpenAI test client wrapper to replace a local LLM, wiring an OpenAI API key, and using the completions API with chat-style messages while measuring tokens and latency.
Functional Testing Using DeepEval - Basic Functionality - Part 116:24
Learn functional testing with the OpenAI test client, focusing on LM-based evaluation metrics like answer relevancy and custom metrics, using natural-language criteria and GPT-4 for evaluation.
Functional Testing Using DeepEval - Basic Functionality - Part 25:57
Learn to configure evaluation parameters for language model tests, set a 0.7 pass threshold, and build instruction-following test cases that verify prompts and responses like listing colors.
Functional Testing Using DeepEval - Basic Functionality - Part 36:01
Functional Testing Using DeepEval - Content Process And Generation6:28
Functional Testing Using DeepEval - Context Learning14:40
Master the context learning test cases with DeepEval, evaluating logical consistency and error handling using defined criteria, combined prompts, and adjustable thresholds for non-deterministic LLM QA.
Non-Functional Testing Using DeepEval - Hallucination Detection7:18

Introduction To RAG6:54
How To Use Contextual Relevancy Metric7:41
Introduction To Our Shoe Store RAG Application4:26
Building And Testing Shoe Store RAG Application - Part 113:07
Building And Testing Shoe Store RAG Application - Part 25:06
Building And Testing Shoe Store RAG Application - Part 38:16
Continue building the shoe store RAG application by implementing the context retrieval function, embedding queries, querying the Pinecone index, and extracting metadata as context documents for answer generation.
Building And Testing Shoe Store RAG Application - Part 45:29
Building And Testing Shoe Store RAG Application - Part 515:36

Requirements

Basic Python knowledge and familiarity with AI/ML concepts

Description

What You'll Learn:

LLM Testing Fundamentals - Master functional and non-functional testing strategies for Large Language Models
Python AI Testing - Build robust test suites using native Python for machine learning applications
DeepEval Framework - Professional LLM evaluation and testing automation with industry-standard tools
RAG Testing - Test Retrieval Augmented Generation systems with Pinecone vector databases
Hallucination Detection - Identify and prevent AI model hallucinations in production environments
Performance Testing - Optimize LLM response times, accuracy, and reliability
Production AI Testing - Real-world testing strategies for enterprise AI applications

Course Content:

Neural Network Architecture - Understand LLM foundations and testing requirements
Comprehensive Testing Types - Functional testing (content processing, logical consistency) and non-functional testing (robustness, performance optimization)
Python Testing Implementation - Hands-on coding from basic concepts to advanced frameworks
DeepEval Mastery - Professional AI testing automation and continuous integration
Real-World Project - Build and test a complete shoe store RAG application with Pinecone integration

Perfect For:

AI Developers building LLM-powered applications
Machine Learning Engineers implementing production AI systems
QA Engineers transitioning to AI testing roles
Python Developers working with GPT, ChatGPT, and OpenAI APIs
Data Scientists validating generative AI models
Software Engineers integrating LLMs into existing applications

Prerequisites:

Basic Python knowledge and familiarity with AI/ML concepts

Why This Course:

Master the critical skills of LLM testing and AI quality assurance that companies desperately need. Learn industry-standard tools like DeepEval, work with cutting-edge technologies like RAG and vector databases, and build portfolio projects that demonstrate real-world AI testing expertise.

Tags: #LLMTesting #AITesting #MachineLearningTesting #PythonAI #DeepEval #RAGTesting #VectorDatabase #Pinecone #AIValidation #MLTesting #GenerativeAI #NLPTesting #AIQualityAssurance #LLMEvaluation #AIAutomation

Who this course is for:

QA Engineers transitioning to AI testing roles
AI Developers building LLM-powered applications
Data Scientists validating generative AI models
Software Engineers integrating LLMs into existing applications

LLM Testing Masterclass: Software QA for AI Language Models

What you'll learn

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Course content

Introduction11 lectures • 1hr 4min

Building LLM Test Suites in Native Python10 lectures • 1hr 54min

Framework-Based Testing: Leveraging DeepEval for Robust LLM Quality Assurance9 lectures • 1hr 25min

Development And Testing Of A RAG System Using Pinecone And DeepEval8 lectures • 1hr 7min

Resources1 lecture0

Requirements

Description

Who this course is for: