ISTQB CT-GenAI: Generative AI for Testers

Generative AI can help you analyze requirements, design tests, and improve automation. But useful results take more than asking a chatbot to “write some test cases.” You need clear prompts, ways to evaluate the output, and the judgment to recognize when AI gets something wrong.

That's why I created ISTQB CT-GenAI Certification: Generative AI for Testers, now available on Udemy. The course connects certification preparation with practical testing tasks, so you can build skills you can use on your next project.

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Who is this course for?

I built this course for software testers, QA engineers, and test automation engineers who want to use GenAI more effectively and prepare for the ISTQB Certified Tester – Testing with Generative AI (CT-GenAI) certification. Test leads and managers will also find guidance on bringing GenAI into their teams.

You should be comfortable with the testing process and basic test design. No prior GenAI experience is assumed—we start with the foundations and build from there.

What you'll learn

The course follows five core areas of CT-GenAI preparation:

1. Understand generative AI for software testing

Learn how large language models work, including tokens, context windows, and why the same prompt can produce different results. Explore reasoning models, multimodal inputs, and the difference between an AI chatbot and an application built for testing.

2. Write better prompts for real testing tasks

Build prompts with clear roles, context, instructions, and constraints. Practice techniques such as few-shot prompting and prompt chaining, then apply them to test analysis, test design, regression automation, and test monitoring.

You'll work through examples such as turning a wireframe into acceptance criteria and generating Gherkin scenarios. You'll also learn to evaluate results and refine prompts when the first answer falls short.

3. Recognize and manage GenAI risks

Learn to spot hallucinations, reasoning errors, and bias in generated output. Explore privacy and security risks, including prompt injection and sensitive test data, alongside approaches for reducing those risks.

4. Explore LLM-powered test infrastructure

Understand how retrieval-augmented generation (RAG), AI agents, and fine-tuning can support testing. Learn where each approach fits and how LLMOps supports the deployment and management of these systems.

5. Bring GenAI into your test organization

Explore model selection, cost considerations, adoption strategy, and the skills teams need. Learn to recognize shadow AI risks and plan how testing processes and responsibilities can evolve.

Practice, evaluate, and remember

The course materials include hands-on worksheets, worked examples, and knowledge checks to help you move from understanding a concept to applying it.

• Try testing scenarios: Practice writing prompts for acceptance criteria, test cases, and automation tasks.
• Review the output: Use self-check criteria to judge whether an AI response is useful and accurate.
• Check your understanding: Pause for retrieval questions and answer from memory before reviewing the explanation.
• Improve your approach: Refine prompts based on the quality of the results, rather than accepting the first response.

The goal is to help you make better testing decisions with AI while keeping your testing judgment central to the work.

Start learning on Udemy

Whether you're preparing for CT-GenAI or building practical GenAI skills for your current role, this course gives you a structured path through the concepts, techniques, and tradeoffs.

Explore the course and enroll using my student discount link. Review the course details and current offer on Udemy before purchasing.

I look forward to helping you take the next step in your testing career.