ISTQB Generative AI (CT‑GenAI) Certification Exam Prep

Name: ISTQB Generative AI (CT‑GenAI) Certification Exam Prep
Rating: 4.4 (192 reviews)

Master Generative AI testing concepts and confidently prepare for the ISTQB Generative AI (CT‑GenAI) exam

Created bySoftware Testing Mentor & RCV Academy, Manish Verma

Last updated 8/2026

English

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What you'll learn

Master Foundations of Generative AI. Understand the role of large language models (LLMs) and generative AI in modern QA.
Comprehensive explanation on Prompt engineering for testers. Master the art of crafting effective prompts to generate reliable test artifacts.
Learn Core principles of Generative AI and how to apply Generative AI safely and effectively in software testing.
Explore how to do Test planning with Generative AI, design structured test plans using Generative AI capabilities.
Use Generative AI for Test designing and test execution. Apply Generative AI approaches to create, automate, and analyse test executions.
Risk management and safeguards: Identify limitations, biases, and risks of Generative AI, and apply mitigation techniques.
Use Generative AI to produce structured test reports and documentation aligned with your enterprise project standards.
Understand compliance, transparency, and responsible use of Generative AI in QA processes.

Course content

13 sections • 97 lectures • 9h 23m total length

Introduction to ISTQB Generative AI Certification Course3:13
In this video, I will do a brief introduction to ISTQB Generative AI Certification Course and what all topics will be covered in this ISTQB GenAI Course.
ISTQB Generative AI v1.1 Certification Syllabus Updates3:47
Who Can Take This Course1:43
Discover who this certification is designed for. From software testers and QA engineers eager to break into AI‑driven testing. Learn how ISTQB CT‑GenAI empowers professionals at all levels.
Career Path for Testers4:16
Explore how Generative AI testing opens new career opportunities. See how ISTQB® CT‑GenAI certification positions you for roles in advanced QA, automation, and AI‑powered software testing.
Business Outcomes2:31
Understand the organizational benefits of Generative AI testing. Learn how certified testers drive efficiency, reduce costs, and improve software quality across industries.
Examinable Learning Objectives5:20
Get a clear overview of the ISTQB Generative AI (CT‑GenAI) syllabus. Learn exactly what’s covered in the exam, from AI foundations to practical testing applications.

1 Introduction to Generative AI for Software Testing1:29
In this module, we will gain a solid foundation in Generative AI and its role in modern software testing. This section introduces the key concepts, technologies, and industry relevance of AI‑driven testing. You’ll explore how Large Language Models (LLMs) are transforming QA practices, setting the stage for your ISTQB® CT‑GenAI certification journey.
1.1 Generative AI Foundations and Key Concepts9:33
Build a strong foundation in Generative AI. Understand the principles, terminology, and key concepts that shape AI‑driven testing.
1.1.1 AI Spectrum: Symbolic AI, Classical Machine & Deep Learning, and GenAI7:07
Trace the evolution of AI technologies. Learn how Generative AI fits into the broader AI spectrum and why it matters for testers.
1.1.2 Basics of Generative AI and LLMs10:36
Discover the fundamentals of Generative AI and Large Language Models (LLMs). Learn how they generate text, code, and test artifacts.
Module 1 Quiz 1
Hands On | Calculate Tokens | Token IDs and Understand Embeddings5:44
In this tutorial, we will put theory into practice with a hands‑on exercise designed to deepen your technical understanding of LLMs. You’ll calculate tokens, explore token IDs, and learn how embeddings represent meaning in AI models. This practical session ensures you can confidently work with the mechanics behind Generative AI, bridging the gap between concepts and application.
1.1.3 Foundation, Instruction-Tuned and Reasoning LLMs6:47
Differentiate between foundation models, instruction‑tuned models, and reasoning LLMs. Understand their unique strengths in testing workflows.
1.1.4 Multimodal LLMs and Vision-Language Models4:28
Explore multimodal AI systems that combine text, images, and other inputs. Learn how vision‑language models expand testing possibilities.
Hands On | Review and Execute a Given Prompt Addressing a Test Task4:49
Explore how to review and execute prompts with a multimodal LLM by combining text and a login page image to generate functional test cases for an e-commerce login feature.
1.2 Leveraging Generative AI in Software Testing: Core Principles2:36
In this module, we will learn how to apply Generative AI effectively in real testing scenarios. This module covers the ISTQB® CT‑GenAI core principles, including prompt engineering, test case generation, defect analysis, and risk management. By the end, you’ll understand how to integrate AI responsibly into your testing workflows to boost efficiency and accuracy.
Module 1 Quiz 2
1.2.1 Key LLM Capabilities for Test Tasks6:06
See how LLMs support test case generation, defect analysis, and automation. Learn practical applications that boost QA productivity.
1.2.2 AI Chatbots and LLM-Powered Testing Applications for Software Testing8:02
Discover how AI chatbots and LLM‑powered tools transform software testing. Learn to integrate them into real‑world QA processes.
Module 1 Quiz 3

2 Prompt Engineering for Effective Software Testing3:05
In this tutorial, learn how prompt engineering improves accuracy, coverage, and efficiency in AI‑driven software testing.
2.1 Effective Prompt Development2:50
In this tutorial, master techniques for crafting clear, context‑rich prompts that yield reliable test outputs.
Hands On | AI Prompting Basics in ChatGPT10:45
Navigate the ChatGPT interface, craft clear prompts with context and role, and define output formats like CSV to support software testing tasks such as test case generation and automation.
2.1.1 Structure of Prompts for Generative AI in Software Testing8:04
In this tutorial, understand how context, instructions, and expected outcomes form effective prompts for testing tasks.
Hands On | Observe and Analyze Prompt Components6:58
Hands On | Observing and Discussing Prompt Chaining, Few-Shot Prompting7:43
Hands On | Identifying Prompt Engineering Techniques in Given Examples2:03
Identify prompt engineering techniques from given examples, including prompt chaining, few-shot prompting, and meta prompting, through hands-on exercises tied to software tester prompts and the login feature.
Hands On | 6 Core Elements of a Good Prompt10:21
Identify the six core components of a good prompt: role, instruction, context, format, constraints, and examples—and apply them to craft prompts for freshers in software testing.
2.1.2 Core Prompting Techniques for Software Testing8:40
In this tutorial, we will discuss core prompting techniques to generate quality test artifacts.
Hands On | Top Prompting Techniques2:58
Master zero-shot, one-shot, and few-shot prompting, chain-of-thought prompting, and compound prompting to build effective prompts for generative AI, with exam-ready practice for the ISTQB GenAI certification.
Hands On | Zero-Shot Prompting6:00
Learn zero-shot prompting, where AI performs tasks with no examples using pre-trained knowledge. Discover best practices—clear prompts, role context, and avoiding ambiguity to reduce hallucinations in testing.
Hands On | One-Shot Prompting6:36
Learn one-shot prompting: provide one instruction plus an input-output example to guide an LM like ChatGPT or Copilot, and apply the pattern to login test cases.
Hands On | Few-Shot Prompting9:20
Engage in a hands-on overview of few-shot prompting, comparing zero, one, and two-or-more example prompts, and learn how to design instructions while considering token costs.
Hands On | Chain-of-Thought Prompting8:34
Explore chain of thought prompting and its step-by-step reasoning in generative AI, with zero-shot and few-shot variants, to improve testing reports, data generation, and root cause analysis.
2.1.3 System Prompt and User Prompt6:26
In this tutorial, we will learn about system prompts and user prompts.
Module 2 Quiz 1
2.2 Applying Prompt Engineering Techniques to Software Test Tasks3:10
In this tutorial, apply prompting strategies to real testing workflows like requirement analysis and test case design.
2.2.1 Test Analysis with Generative AI11:26
In this tutorial, we will learn how Generative AI supports test analysis by identifying test conditions and refining requirements.
Hands On | Practice Creating Structured Multimodal Prompts4:36
Practice creating multimodal prompts that combine text and GUI wireframe to generate acceptance criteria for a user story. See how input data and wireframes shape outcomes using six ISTQB criteria.
Hands On | Practice Prompt Chaining and Human Verification9:07
Practice prompt chaining and human verification to analyze user stories, refine acceptance criteria, and assess testability and completeness using large language models within agile testing.
2.2.2 Test Design and Test Implementation with Generative AI10:53
In this tutorial, we will learn how AI-driven Test Design Techniques help testers to create and implement effective test cases.
Hands On | Practice Functional Test Case Generation from User Stories8:07
Hands On | Use the Few-Shot Prompting Technique to Generate Gherkin Style Test4:59
Hands On | Use Prompt Chaining to Prioritize Test Case within a Given Test Suite8:57
learn to use prompt chaining with generative ai to prioritize test cases in a given test suite, considering risk, coverage, and dependencies.
2.2.3 Automated Regression Testing with Generative AI10:51
In this tutorial, we will learn how AI enhances regression testing by automating execution and detecting unintended changes.
Hands On | Few-Shot Prompting for Test Automation3:08
Practice few-shot prompting to create and manage keyword-driven test scripts for a web application using a GUI automation framework, copilot in VS Code, and LLM tool to generate test cases.
Hands On | Practice Writing Structured Prompts for Test Report Analysis10:07
Practice writing structured prompts for test report analysis with AI, compare regression test results with specifications, cluster defects, and identify anomalies to deliver actionable insights for stakeholders.
Module 2 Quiz 2
2.2.4 Test Monitoring and Test Control with Generative AI6:31
In this tutorial, we will learn how AI enables test monitoring and test control through real-time dashboards and adaptive reporting.
Hands On | Observe Test Monitoring Metrics Prepared by AI from Test Data8:03
conduct a hands-on exercise to observe test monitoring metrics generated by ai from test data, covering test progress, defect trends, coverage, and risks with chart visualizations.
2.2.5 Choosing Prompting Techniques for Software Testing10:04
In this tutorial, we will learn how to master effective prompting strategies to generate reliable test artifacts.
Hands On | Selecting Context-Appropriate Prompting Techniques for Given Test6:35
Module 2 Quiz 3
2.3 Evaluate Generative AI Results and Refine Prompts2:31
In this tutorial, we will learn structured approaches to assess AI outputs and refine prompts for better test effectiveness.
2.3.1 Metrics for Evaluating the Results of Generative AI on Test tasks13:21
In this tutorial, we will learn how to apply test metrics to measure accuracy, coverage, and defect detection in AI-generated assets.
Hands On | Observe How Metrics can be Used for Evaluating the Result3:12
2.3.2 Techniques for Evaluating and Iteratively Refining Prompts8:44
In this tutorial, we will learn how to use iterative refinement to optimize prompts and improve AI-driven test results.
Hands On | Evaluate and Optimize a Prompt for a Given Test Task5:20
Evaluate and optimize prompts for a test task by refining prompts with roles, context, constraints, and output formats to improve test case quality through ab testing.
Module 2 Quiz 4

3 Managing Risks of GenAI in Software Testing2:31
3.1 Hallucinations, Reasoning Errors and Biases3:46
In this tutorial, we will learn how to identify risks of defects in AI outputs caused by hallucinations and biases.
3.1.1 Hallucinations, Reasoning Errors and Biases in Generative AI9:25
In this tutorial, we will learn how AI-generated content can deviate from valid test oracles.
3.1.2 Identify Hallucinations, Reasoning Errors and Biases in LLM Output9:07
In this tutorial, we will learn techniques to detect invalid or misleading outputs during test execution.
Hands On | Experiment with GenAI Hallucinations Related to Software Test Task3:56
Module 3 Quiz 1
3.1.3 Mitigation techniques of GenAI hallucinations, reasoning errors and biases8:37
In this tutorial, we will learn how to apply risk-based testing to mitigate AI-related errors.
3.1.4 Mitigation of Non-Deterministic Behavior of LLMs5:46
In this tutorial, we will learn strategies to handle non-deterministic test results from LLMs.
3.2 Data Privacy and Security Risks of Generative AI in Software Testing2:57
In this tutorial, we will learn how to assess security testing concerns when using AI in test environments.
3.2.1 Data Privacy and Security Risks Associated with Using Generative AI10:07
In this tutorial, we will learn about data privacy and security risks associated with using Generative AI
Module 3 Quiz 2
3.2.2 Data Privacy and Vulnerabilities in GenAI for Test processes and Tools8:12
In this tutorial, we will learn about risks in test tools and processes when handling sensitive data.
3.2.3 Mitigation Strategies to Protect Data Privacy and Enhance Security.10:44
In this tutorial, we will learn how to apply test control measures to safeguard data privacy and security.
Hands On | Data Privacy and Security Risks in Generative AI3:03
Module 3 Quiz 3
3.3 Energy Consumption and Environmental Impact of GenAI in Software Testing3:48
In this tutorial, we will learn how to evaluate sustainability risks by analyzing AI’s impact on test environment resources.
3.3.1 The Impact of Using GenAI on Energy Consumption and CO2 Emissions3:52
In this tutorial, we will learn how AI-driven testing impacts energy consumption and CO2 Emissions
Hands On | Use a Simulator to Calculate The Energy and CO₂ Emissions3:47
Calculate the energy use and CO₂ emissions of generative AI prompts by using online energy usage estimators, counting input and output tokens, and assessing model and hardware costs.
3.4 AI Regulations, Standards, and Best Practice Frameworks2:41
In this tutorial, we will learn how to ensure compliance with standards and best practices in AI-enabled testing.
3.4.1 AI Regulations, Standards and Frameworks Relevant to GenAI in S/W Testing6:55
In this tutorial, we will learn about regulatory frameworks guiding test governance in AI contexts.
Module 3 Quiz 4

4 LLM-Powered Test Infrastructure for Software Testing1:55
Hands On | Module 45:43
Explore hands-on module 4 exercises on retrieval augmented generation, LLM powered agents, and fine tuning, including how attaching data and wireframes refine test case generation and automation tasks.
4.1 Architectural Approaches for LLM-Powered Test Infrastructure3:39
In this tutorial, we will learn how to design test environment architecture for integrating LLMs into workflows.
4.1.1 Key Architectural Components & Concepts of LLM-Powered Test Infrastructure11:28
In this tutorial, we will learn about core test infrastructure components supporting AI-driven testing.
4.1.2 Retrieval-Augmented Generation11:09
In this tutorial, we will learn how RAG enhances AI-generated test artifacts.
Module 4 Quiz 1
4.1.3 The Role of LLM-Powered Agents in Automating Test processes11:20
In this tutorial, we will learn how LLM agents are used to automate test execution and test management.
4.2 Fine-Tuning and LLMOps: Operationalizing Generative AI for Software Testing0:56
In this tutorial, we will learn how test process improvement is achieved through fine-tuning and operationalizing LLMs.
4.2.1 Fine-Tuning LLMs for Test tasks7:03
In this tutorial, we will learn how to customize LLMs for specific test objectives.
4.2.2 LLMOps when Deploying and Managing LLMs for Software Testing9:21
In this tutorial, we will learn about LLMOps
Module 4 Quiz 2

5 Deploying and Integrating Generative AI in Test Organizations1:01
Explore deploying and integrating generative AI in test organizations through an adoption roadmap, shadow AI risks, and strategy criteria for selecting language models, while managing change.
5.1 Roadmap for the Adoption of Generative AI in Software Testing3:58
In this tutorial, we will learn how to develop a test strategy for adopting AI across the lifecycle.
5.1.1 Risks of Shadow AI5:59
In this tutorial, we will learn how to identify risks of shadow AI
5.1.2 Key Aspects of a Generative AI Strategy in Software Testing5:52
In this tutorial, we will learn key aspects of a GenAI strategy in Software Testing
Module 5 Quiz 1
5.1.3 Selecting LLMs/SLMs for Software Test Tasks7:12
In this tutorial, we will learn criteria for choosing SLMs/LLMs
Hands On | Estimating the Recurring Costs of Using Generative AI5:38
Calculate the recurring costs of using generative AI for testing tasks by estimating input and output tokens and applying model pricing.
5.1.4 Phases when Adopting Generative AI in Software Testing7:27
In this tutorial, we will learn about phases when adopting GenAI in Software Testing
5.2 Manage Change when Adopting Generative AI for Software Testing1:48
In this tutorial, we will learn how to manage organizational change for AI-enabled testing.
Module 5 Quiz 2
5.2.1 Essential Skills and Knowledge for Testing with Generative AI7:03
In this tutorial, we will learn the tester competencies required for AI-driven testing.
5.2.2 Building Generative AI Capabilities in Test Teams6:27
In this tutorial, we will learn how to develop test team skills to leverage AI effectively.
5.2.3 Evolving Test Processes in AI-Enabled Test Organizations9:22
In this tutorial, we will learn how to adapt test processes to align with AI-enabled practices.
Module 5 Quiz 3

Requirements

Anyone can take this course. No prior experience needed
To gain ISTQB CT-GenAI certification, you must hold the Certified Tester Foundation Level (CTFL) certificate

Description

Unlock the future of software testing with ISTQB Generative AI (CT‑GenAI) Certification Exam Prep Comprehensive Course. This is the ultimate training program designed to help you master Generative AI testing concepts and confidently pass the ISTQB CT‑GenAI exam.

Whether you’re a fresher looking to get into software testing, already working in IT and looking for career change, a QA professional, or part of a corporate testing team, this course equips you with the skills, strategies, and exam readiness needed to thrive in today’s AI‑driven testing landscape.

This course not only prepares you for the ISTQB Generative AI (CT‑GenAI) exam but also empowers you with practical insights into how Generative AI is reshaping quality assurance. By the end, you’ll be equipped with the knowledge, confidence, and certification credentials to stand out in a competitive job market and accelerate your career growth.

What you’ll learn in this Course

ISTQB Generative AI (CT‑GenAI) syllabus covered in very simple and practical examples
Generative AI testing techniques and their application in real projects
Exam preparation strategies with practice questions and mock tests
Industry insights to future‑proof your QA and testing career

Who can take this Course

Professionals preparing for the ISTQB CT‑GenAI certification exam
Software testers, QA engineers, and developers looking to upskill in AI testing
Anyone looking to change career into the fast‑growing field of Generative AI testing
Corporate teams seeking structured training aligned with ISTQB Generative AI Certification standards

Who this course is for:

Software Testers / QA Engineers / SDETs
QA Leads / Test Leads / Test Managers / Quality Managers
Business Analysts / Software Development Managers / Project Managers
Operations Team Members
IT Directors
Management Consultants

ISTQB Generative AI (CT‑GenAI) Certification Exam Prep

What you'll learn

Explore related topics

Course content

Introduction6 lectures • 21min

Introduction to Generative AI for Software Testing11 lectures • 1hr 7min

Download PPT Slides for Introduction to Generative AI for Software Testing1 lecture • 1min

Prompt Engineering for Effective Software Testing35 lectures • 4hr 10min

Download PPT Slides for Prompt Engineering for Effective Software Testing1 lecture • 1min

Managing Risks of Generative AI in Software Testing17 lectures • 1hr 39min

Download PPT Slides for Managing Risks of Generative AI in Software Testing1 lecture • 1min

LLM-Powered Test Infrastructure for Software Testing9 lectures • 1hr 3min

Download PPT Slides for LLM-Powered Test Infrastructure for Software Testing1 lecture • 1min

Deploying and Integrating Generative AI in Test Organizations11 lectures • 1hr 2min

Requirements

Description

Who this course is for: