Name: ISTQB Testing with Generative AI (CT-GenAI)
Rating: 4.6 (103 reviews)

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Created byTarek Roshdy

Last updated 4/2026

English

What you'll learn

Explain how Generative AI and Large Language Models (LLMs) can support different software testing activities
Use Gen-AI tools safely and effectively to create high-quality testing assets such as test cases, user stories, acceptance criteria, and test charters
Apply prompt engineering techniques to improve the accuracy, structure, and relevance of AI-generated testing outputs
Evaluate AI-generated results for correctness, completeness, bias, traceability, and compliance with organizational standards

Course content

15 sections • 94 lectures • 6h 16m total length

Introduction & Course Content4:56

Chapter 1 Content2:10
Introduction to Chapter 10:34
GenAI & LLM Definition4:06
Tokenization1:35
Context Windows4:02
Multimodal Models1:18
Testing Tasks supported by LLMs1:44
AI Spectrum7:49
SLMs & LLMs2:01
Tokenization & Embeddings2:20
Transformer Model2:47
Non-deterministic Behavior4:44
Hands-On Exercise: Tokenization17:02
Foundation, Instruction-tuned, & Reasoning LLMs4:37
Multimodal LLMs & Vision Language Models3:14
Leveraging Generative AI in Software Testing1:52
Key LLM Capabilities for Test Tasks2:53
AI Chatbots VS LLM-Powered Testing Apps2:57
Questions 1 & 2 ISTQB Official Sample Exam4:17
Questions 3 to 5 ISTQB Official Sample Exam6:16

Chapter 2 Content6:46
Introduction to Chapter 20:46
Effective Prompt Development1:28
Structure of Prompts for Generative AI in Software Testing6:44
Prompt Chaining1:57
Few-Shot Prompting2:30
Meta Prompting2:27
System Prompt3:56
User Prompt1:06
Where GenAI can help in Testing?1:48
How Prompt Engineering Improves results?1:00
Test Analysis with Generative AI7:09
Test Design and Test Implementation with Generative AI6:24
Automated Regression Testing with Generative AI4:07
Test Monitoring & Test Control with Generative AI3:14
Choosing Prompting Techniques for Software Testing3:42
Evaluate Generative AI Results & Refine Prompts for Software Test Tasks2:54
Metrics for Evaluating the Results of Generative AI on Test Tasks10:09
Techniques for Evaluating & Iteratively Refining Prompts2:13
Chapter 2 Summary0:22
Questions 6 to 10 ISTQB Official Sample Exam9:45
Questions 11 to 13 ISTQB Official Sample Exam9:10
Questions 14 to 15 ISTQB Official Sample Exam8:16
Questions 16 to 18 ISTQB Official Sample Exam8:18

Chapter 3 Content2:18
Introduction to Chapter 30:31
Hallucinations, Reasoning Errors, & Biases in Generative AI2:48
Hallucination Detection in LLM Output1:11
Reasoning Error Detection in LLM Output1:01
Bias Detection in LLM Output1:43
Mitigation Techniques of GenAI Hallucinations, reasoning errors and biases3:43
Mitigation of Non-deterministic behavior of LLMs6:12
Data Privacy & Security Risks Associated with using Generative AI2:49
Data Privacy & Vulnerabilities in Generative AI for Test Processes & Tools2:45
Mitigation Strategies to Protect Data Privacy & Enhance Security in GenAI5:45
Energy Consumption & Environmental Impact of Generative AI in Testing5:25
AI Regulations, Standards, and Best Practice Frameworks3:55
Chapter 3 Summary0:33
Questions 19 to 21 ISTQB Official Sample Exam5:19
Questions 22 to 25 ISTQB Official Sample Exam5:56
Questions 26 to 28 ISTQB Official Sample Exam3:43

Chapter 4 Content1:08
Introduction to Chapter 40:26
Key Architectural Components & Concepts of LLM-Powered Test Infrastructure4:53
Retrieval-Augmented Generation (RAG)5:00
The Role of LLM-Powered Agents in Automating Test Processes5:04
Fine-Tuning LLMs for Test Tasks2:17
LLMOps when deploying and managing LLMs for Software Testing4:22
Chapter 4 Summary0:19
Questions 29 to 33 ISTQB Official Sample Exam7:52

Chapter 5 Content1:26
Introduction to Chapter 50:49
Roadmap for Adopting GenAI in Testing1:29
What is Shadow AI?4:17
Key Aspects of a Generative AI Strategy in Software Testing4:23
Selecting LLMs/SLMs for Software Test Tasks3:59
Phases when Adopting Generative AI in Software Testing3:19
Manage Change when Adopting Generative AI in Software Testing2:40
Building Generative AI Capabilities in Test Teams3:48
Evolving Test Processes in AI-Enabled Test Organizations3:50
Chapter 5 Summary0:34
Questions 34 to 36 ISTQB Official Sample Exam4:29
Questions 37 to 40 ISTQB Official Sample Exam5:49

Requirements

Passing the ISTQB Foundation Level Exam

Description

Generative AI and Large Language Models LLMs are transforming the software testing profession Testers today are expected to work faster deliver higher quality and adapt to rapid development cycles and AI is now a key enabler of productivity creativity and smarter decision making

This course is designed specifically for software testers QA engineers test managers and Agile teams who want to leverage AI tools responsibly and effectively throughout the testing lifecycle You will learn how to unlock the real power of Gen AI and LLMs to improve test planning accelerate test case design support automation enhance documentation and optimize communication with stakeholders

Throughout this practical hands on training you will

Understand the role of Generative AI in testing and how it aligns with ISTQBs Gen AI guidance

Apply proven prompt engineering strategies to generate accurate and traceable test assets

Automate repetitive work such as writing test cases acceptance criteria and test data

Analyze and validate AI generated outputs to ensure correctness and reduce errors

Identify risks limitations ethical considerations and quality controls when using AI in testing

Integrate AI support into Agile CI CD and modern test management workflows

The course includes real examples case studies and practical exercises that help you apply AI immediately in your daily testing activities with measurable results

No prior AI experience is required By the end of this course you will be empowered to work smarter with AI increase your efficiency as a tester and stay competitive in the future of software quality

Enroll now and upgrade your testing career with Generative AI

The course includes real examples case studies and practical exercises that help you apply AI immediately in your daily testing activities with measurable results

No prior AI experience is required By the end of this course you will be empowered to work smarter with AI increase your efficiency as a tester and stay competitive in the future of software quality

Enroll now and upgrade your testing career with Generative AI

Who this course is for:

QA Engineers
Software Testers
AI Testers
QA Managers

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 5min

Chapter 1: Introduction to Generative AI for Software Testing20 lectures • 1hr 18min

Chapter 1 Questions0

Chapter 2: Prompt Engineering for Effective Software Testing24 lectures • 1hr 46min

Chapter 2 Questions0

Chapter 3: Managing Risks of Generative AI in Software Testin17 lectures • 56min

Chapter 3 Questions0

Chapter 4: LLM-Powered Test Infrastructure for Software Testing9 lectures • 31min

Chapter 4 Questions0

Chapter 5: Deploying and Integrating Generative AI in Test organizations13 lectures • 41min

Requirements

Description

Who this course is for: