ISTQB® CT-GenAI Certification: Testing with Generative AI — Your Complete Guide 2026

What is the ISTQB® CT-GenAI certification?

The ISTQB® CT-GenAI certification (Certified Tester – Testing with Generative AI) is part of the ISTQB® Specialist track and was first released in 2024. It differs fundamentally from the longer-standing ISTQB® CT-AI certification (Certified Tester AI Testing), even though both belong to the AI field. While CT-AI focuses on how you test AI-based systems—that is, the quality assurance of machine learning models and AI applications—CT-GenAI teaches you how to use generative AI tools (LLMs, prompt engineering, AI assistants) in the testing process itself.

Simply put: CT-AI = Testing AI. CT-GenAI = Testing WITH AI. Both complement each other and are taken sequentially by many testers. The CT-GenAI course lasts three days and concludes with an optional multiple-choice exam that you can take directly on the last day of training. The exam covers the five modules of the syllabus and assesses your practical understanding of all phases—from the first prompt to the organizational implementation of generative AI within the testing team.

Why is CT-GenAI so relevant right now?

Two developments are driving the demand for CT-GenAI-certified testers in parallel. On the one hand, more and more teams are actively experimenting with generative AI in testing—often in an unstructured manner, without clear governance, and without defined quality standards. On the other hand, the EU AI Act establishes a regulatory framework that legally mandates precisely such structured competencies. Companies using AI in testing today need testers who know how to do so safely, efficiently, and in compliance with regulations. The CT-GenAI certification is currently the only official qualification that validates this exact skill set.

The ISTQB® CT-GenAI Syllabus in Detail — The 5 Modules

The official CT-GenAI Syllabus (Version 1.0) is divided into five modules that build on one another. Each module combines theoretical fundamentals with practical exercises, so that by the end, you’ll not only understand what Generative AI can do in testing, but also how to apply it in practice. Below, we provide an overview of what you’ll cover in each module.

Module 1 — Introduction to Generative AI for Software Testing

The first module lays the technical foundation for using generative AI in software testing. You’ll learn how symbolic AI, classical machine learning, deep learning, and generative AI differ from one another and how large language models (LLMs) work in general. This includes key concepts such as tokenization, embeddings, context windows, Transformer models, and the non-deterministic behavior of LLMs. You will also distinguish between Foundation LLMs, Instruction-Tuned LLMs, and Reasoning LLMs, and learn how multimodal LLMs and vision-language models can utilize text and image inputs for testing tasks. In the testing context, the focus is also on which functions LLMs can support—such as requirements analysis, test case generation, test data, test automation, and result analysis—and how AI chatbots differ from integrated LLM-powered testing applications.

Module 2 — Prompt Engineering for Effective Software Testing

Prompt engineering is the most comprehensive module of the CT GenAI syllabus. You will learn how structured prompts for testing tasks are constructed and what role components such as role, task, context, input data, output format, and constraints play. The syllabus covers key prompting methods such as zero-shot, one-shot, and few-shot prompting, prompt chaining, and meta-prompting, as well as the difference between system prompts and user prompts. You will then apply these methods to specific software testing tasks: test analysis, test design, test implementation, automated regression testing, as well as test monitoring and test control. Another focus is on systematically evaluating LLM results, using appropriate metrics, and iteratively refining prompts until the generated test artifacts better align with the test objective.

Note: If you are looking for approaches such as Chain-of-Thought or Role-Prompting, the AiU Certified GenAI-Assisted Test Engineer course is a better fit for you.

Module 3 — Risk Management for GenAI in Testing

This module addresses the risks associated with using generative AI in software testing and shows how testers can manage them. The focus is on hallucinations, reasoning errors, and biases in LLM outputs, as well as methods for identifying and mitigating these risks. Additionally, the syllabus covers the non-deterministic behavior of LLMs, which can cause identical or similar inputs to produce different results. Another key focus is on data protection and security risks, such as those arising from sensitive information in prompts or vulnerabilities in GenAI-supported testing processes and tools. You will also learn how task characteristics and model usage can influence energy consumption and CO₂ emissions, and which AI regulations—such as the EU AI Act—as well as standards and best-practice frameworks are relevant for the responsible use of generative AI in testing.

Module 4 — LLM-powered testing infrastructure

The fourth module focuses on how generative AI can be technically integrated into test infrastructures. You will learn about the key architectural components and concepts of LLM-powered test infrastructures and understand how such systems can process test-related queries, analyze requirements, generate test cases, or evaluate results. A central topic is Retrieval-Augmented Generation (RAG), which integrates additional knowledge sources to better align LLM outputs with the relevant test context. The module also covers LLM-powered agents that can support or automate recurring test tasks. The module is rounded out by the fine-tuning of language models for specific testing tasks as well as LLMOps—that is, the deployment, management, and monitoring of LLMs in testing operations.

Module 5 — Introducing GenAI into Testing Organizations

The final module covers the organizational implementation of generative AI in test teams and test organizations. You will learn about the risks that can arise from shadow AI when teams use unapproved AI tools without clear guidelines—such as data protection, security, compliance, or copyright risks. Building on this, the syllabus covers the key aspects of a GenAI testing strategy: Test objectives, selection of suitable LLMs or SLMs, quality of input data, regulatory requirements, costs, and roadmap. It also covers the phases of implementation, building the necessary skills in test teams, and changing test processes, roles, and responsibilities. The module thus demonstrates not only how GenAI is used technically, but also how organizations can embed its use in a sustainable, controlled, and value-adding manner.

Who benefits from the CT-GenAI certification?

The CT-GenAI certification is not aimed at a single target group, but at an entire ecosystem of roles that collectively ensure the quality of modern software. The largest group consists of traditional software testers and QA engineers who already possess foundational-level knowledge and want to expand their toolkit to include generative AI. They benefit immediately because they can integrate GenAI directly into their daily work—creating test cases faster, generating more realistic test data, and analyzing defects with greater precision.

Equally exciting is the certification for test automation engineers who want to integrate generative AI into their test automation. In CT-GenAI, they learn how LLM-powered test applications can be embedded into existing test frameworks, how GenAI supports automated regression testing, test scripts, and test reports, and which infrastructure concepts—such as RAG, LLM-powered agents, and LLMOps—play a role in this process. For test managers and QA leads, the fifth module is crucial because it addresses the organizational dimension of GenAI implementation—including governance, role models, and change management.

Developers working in testing-related roles (backend, embedded, web) will also find the CT-GenAI a quick way to methodically deepen their understanding of testing while gaining up-to-date insights into AI. Product owners, Scrum Masters, and business analysts will also benefit if they want to understand where their teams can use GenAI effectively—and where they need to actively address risks.

Prerequisites for Participation

We recommend that you have already completed the ISTQB® Foundation Level, as the CT-GenAI training requires a basic understanding of testing methodology—terms such as equivalence classes, boundary value analysis, regression testing, and risk-based testing are assumed. If you haven’t completed the Foundation Level yet, we recommend starting there first and then choosing CT-GenAI as a follow-up. By the way, you don’t need in-depth machine learning knowledge—we start with the basics of generative AI and build systematically from there.

The ISTQB® CT-GenAI Exam — Structure and Tips

The CT-GenAI exam is optional, but most participants sign up for it because only the certificate provides the full career benefit. The exam takes place on the last day of training, lasts about 60 minutes, and consists of multiple-choice questions covering all five syllabus modules. The questions test less rigid factual knowledge and more your understanding of application—for example: Which prompt type is most suitable in a specific test situation? Or: Which governance control mechanism corresponds to which risk?

The exam fee is currently 225 euros plus VAT and is billed separately from the training. If you don’t pass on your first attempt, you can retake the exam—though experience shows this rarely happens. Pass rates at trendig are significantly above the ISTQB average because we specifically incorporate exam simulations and sample questions into our training. You can find more about ISTQB failure rates and how to overcome exam anxiety in our blog on ISTQB exam preparation.

The most important exam tips at a glance

First: Read each question twice. The CT GenAI questions are often phrased in such a way that a small difference in wording leads to a different correct answer—such as “most suitable” vs. “absolutely necessary.” Second: Actively work through the practical examples from the training, not just the slides. The exam tests your understanding of how concepts are applied, not just definitions. Third: For risk management questions, it helps to think of a specific use case from your everyday life—if you can link the answer to a real-world project, it’s usually correct.

CT-GenAI vs. CT-AI — which certification is right for you?

One of the most common questions participants ask us is: Should I take CT-AI first or CT-GenAI first? The honest answer is: It depends on your goal. If you work at a company that develops its own AI-based products—such as machine learning models, computer vision systems, or autonomous agents—CT-AI is the logical first step. There, you’ll learn how to test such systems, what their specific quality dimensions are (bias, fairness, robustness, explainability), and how to validate them.

If, on the other hand, your focus is on the use of generative AI in everyday testing—that is, on how to use LLMs for your testing work—then CT-GenAI is the better choice. Both certifications build on each other but can be taken independently. Many of our participants take both one after the other—often with a gap of six to twelve months—and thus build a comprehensive AI testing profile that commands significantly above-average pay in the market.

Direct comparison at a glance

Career opportunities with CT-GenAI certification

Market demand for testers with proven AI expertise has skyrocketed over the past two years – and this is directly reflected in salaries. Job postings specifically seeking experience in prompt engineering, LLM integration, or GenAI testing typically offer an annual salary that is 5,000 to 12,000 euros higher in the German market than comparable testing roles without this specialization. For many recruiters, the CT-GenAI certification is a door opener because it validates systematic knowledge rather than just self-assessments.

New roles are also emerging around GenAI in testing: AI Test Engineer, GenAI QA Lead, Test Automation Architect with a focus on AI, or AI Governance Lead in larger companies. For team leaders, CT-GenAI paves the way to a strategic AI role within their organization – that is, moving from pure test management to responsibility for transformation. Anyone who wants to be in a senior or architecture role in five years is significantly better positioned with an AI certification profile than without one.

How to best prepare for the CT-GenAI exam

The best way to prepare for CT-GenAI is a combination of structured training, hands-on experiments, and targeted exam simulations. Pure self-study using the public syllabus is theoretically possible, but experience shows that it is difficult—because the practical components (prompt engineering, tool integration, risk assessment) can only be truly mastered through real hands-on work. That is why most ISTQB partners recommend accredited training with integrated exam preparation.

At trendig, preparation runs in three parallel phases. First phase: the technical and methodological content of the five modules, which we systematically work through over 3 days. Second phase: practical exercises with real LLMs, where you write prompts for actual test tasks and receive immediate feedback. Third track: exam simulations with sample questions based on the ISTQB standard, so that on exam day you’re not only confident in your knowledge but also familiar with the question format. All three tracks together explain our high pass rates. If you want to delve deeper, you’ll find further insights into exam psychology in our blog post on ISTQB failure rates.

The ideal learning path for the next 4 weeks

If you have four weeks and are specifically preparing for CT-GenAI, we recommend the following path: Week 1 — Read through the syllabus and compare it with your own daily project work (Where would you use GenAI? Where do you see risks?). Week 2 — Complete the training, take structured notes. Week 3 — Develop your own prompt templates and use them to solve at least three real-world test tasks. Week 4 — Go through exam simulations and specifically review weak areas. If you follow this path, you are highly likely to pass the exam on your first attempt.

Conclusion: CT-GenAI as a strategic investment in your testing future

The ISTQB® CT-GenAI certification is more than just another certificate on your resume. It is the structured path to professionally leveraging what is perhaps the greatest disruption in software testing over the past decade. Generative AI is here to stay—it will become a standard tool for any serious QA role in the coming years. Those who lay the methodological groundwork today will be in a significantly stronger position in three years than someone who uses GenAI only ad hoc and unstructured.

At trendig, we’ll guide you along this path—with a hands-on 3-day training course, accredited exam preparation, and access to a network of testers and teams who have already taken this very step. Whether you’re getting certified individually or bringing your entire team along through in-house training—we’ll build the right learning path for you. You can find all current dates, prices, and formats on our CT-GenAI training page.