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ISTQB CT-GenAI Certification Prep GenAI for Testers
Published 9/2026
Created by Scottie Crump
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 42 Lectures ( 7h 17m ) | Size: 6.9 GB
Master GenAI for software testing - prompt engineering, risk, LLM test infrastructure & strategy.
What you'll learn
Prepare for the ISTQB Certified Tester GenAI (CT-GenAI v1.1) exam with coverage of every learning objective Explain how LLMs generate output - and why "likely" is not the same as "true" Write, chain and refine prompts that produce usable test cases, Gherkin and regression assets Apply GenAI across test analysis, test design and implementation, regression, and monitoring and control Spot and mitigate hallucinations, reasoning errors and bias in model output Manage data privacy, prompt injection, data poisoning and non-deterministic behavior Design LLM-powered test infrastructure using RAG, agents, fine-tuning and LLMOps Build a GenAI testing strategy and adoption roadmap for your test organizationRequirements
ISTQB Foundation Level certification or equivalent hands-on software testing experience No prior AI or machine learning background needed; every GenAI concept is taught from the ground up Access to any general-purpose LLM chat tool for the hands-on exercises (free tiers are fine)Description
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 a syllabus learning objective at its exam K-level (K1 recall, K2 explain, K3 apply). able hands-on worksheets with copy-ready prompts, expected output, and a self-check rubric, so non-deterministic results still give you actionable feedback. Exam-style knowledge checks after every chapter, with answer reveals and full justifications. Chapter intro and retrieval-based recap videos that quiz you rather than restate - including spaced retrieval from earlier chapters. A searchable 58-term glossary with flashcard mode, using exact ISTQB terminology explained in easy-to-understand terms.If you hold ISTQB Foundation Level and you want to be the person on your team who uses GenAI with judgment instead of hope, start here.
Who this course is for
Testers and QA engineers preparing for the ISTQB Certified Tester GenAI (CT-GenAI) certification SDETs and automation engineers who want to apply GenAI to test design, implementation and regression Test leads and QA managers building a GenAI testing strategy and adoption roadmap for their teams Anyone in software quality who wants structured, vendor-neutral GenAI skills instead of scattered tips and tricksHomepage
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