ISTQB CT-GenAI Certification Prep: GenAI for Testers

About this course

This course covers the complete CT-GenAI v1.1 syllabus and prepares you to sit the exam with confidence.

You will go well beyond "ask a chatbot to write test cases.

" Across 37 lessons in 5 chapters, you'll learn how LLMs actually produce output, how to engineer prompts that yield usable test artifacts, how to catch the hallucinations and reasoning errors these models generate, how RAG, agents, fine-tuning, and LLMOps fit into real test infrastructure, and how to build a GenAI adoption strategy your organization can defend.

What's inside

Chapter 1 — Fundamentals of GenAI and LLMs: the AI spectrum, how LLMs work, foundation vs. instruction-tuned vs. reasoning models, multimodal and vision-language models, and where chatbots end and LLM-powered testing applications begin.

Chapter 2 — Prompt Engineering for Testing (the largest chapter): anatomy of a prompt, core prompting techniques, system vs. user prompts, then GenAI applied across test analysis, test design and implementation, automated regression testing, and test monitoring and control — plus how to choose a technique and measure the result.

Chapter 3 — Managing the Risks of GenAI: defining, spotting, and mitigating hallucinations, reasoning errors, and bias; taming non-determinism; data privacy and security; prompt injection, data poisoning, and malicious code; energy and environmental impact; AI regulations and frameworks.

Chapter 4 — LLM-Powered Test Infrastructure: architecture, retrieval-augmented generation (RAG), LLM-powered agents, fine-tuning for test tasks, and LLMOps.

Chapter 5 — Strategy and Adoption: the risks of shadow AI, building a GenAI testing strategy, selecting LLMs and SLMs, adoption phases, the skills testers need, growing team capability, and how test roles evolve.

How you'll practice

Every lesson maps to

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