eLearning: ISTQB CT-GenAI
Build practical skills in testing with generative AI and prepare for the ISTQB CT-GenAI exam. Learn at your own pace with 58 video lessons, hands-on exercises, worked solutions and revision resources. Exam voucher included.
Planning to study and sit the exam soon? Choose 90-day access for $599. Want more time to learn and revisit the material? Choose 1-year access for $699. Both options include the full course and an exam voucher.
90-Day Access
1-Year Access
CT-GenAI training · Self-paced
Prepare for the ISTQB CT-GenAI exam and build practical skills in using generative AI for software testing. Learn to create and review test cases, recognize unreliable answers and contribute to your team’s use of AI.
Prepare at your own pace with 58 video lessons, chapter flashcards and sample exams.
Build your skills through 15 core course chapters, organized into five connected stages. Prepare for CT-GenAI while practicing how to use and check AI in your test work.
Start Here: Your first win and study plan. Then follow the five stages below.
In practiceInspect tokens and compare the size of different testing inputs.
In practiceGenerate test cases, find missing coverage and check AI-written test reports.
In practiceCompare model answers and identify hallucinations, reasoning errors and bias.
In practiceExperiment with RAG. Watch demonstrations of agents and fine-tuning.
In practiceEstimate recurring model costs and compare options for a testing task.
Follow all five CT-GenAI syllabus areas. Revisit lessons, review chapter flashcards and work through sample exams.
Create and check test cases, identify unreliable answers and compare model costs using realistic testing tasks.
See a real model run → Try the task → Compare with a worked solution.
A fictional booking application connects the examples throughout the course.
Orientation, revision resources and course completion accompany the 15 core chapters. CTFL is required to take the CT-GenAI exam. Certification requires passing the exam.
Start Here: Your first win and study plan
VID-1.1.1-01 Recognize the AI spectrum in testing tools
VID-1.1.2-01 How LLMs generate text and represent meaning
VID-1.1.2-02 Tokens, context windows, and test inputs
Activity: Inspect tokens and compare input sizes
Solution: Inspect tokens and compare input sizes
VID-1.1.3-01 Choose a model type for a testing task
VID-1.1.4-01 Use a wireframe and text in a multimodal prompt
Activity: Run a multimodal test prompt
Solution: Run a multimodal test prompt
VID-1.2.1-01 Match LLM capabilities to testing work
VID-1.2.2-01 Compare chatbots and integrated testing applications
Keywords and Terms
VID-2.1.1-01 Build a structured testing prompt
VID-2.1.1-02 Inspect prompt components in worked examples
Activity: Observe structured prompts
Solution: Observe structured prompts
VID-2.1.2-01 Compare zero-shot, one-shot, and few-shot prompting
VID-2.1.2-02 Observe prompt chaining and meta prompting
Activity: Observe prompting techniques
Solution: Observe prompting techniques
VID-2.1.2-03 Recognize techniques and explain their tradeoffs
Activity: Identify prompting techniques
Solution: Identify prompting techniques
VID-2.1.3-01 Separate system behavior from the user task
VID-2.2.1-01 Turn a test basis into analysis tasks
VID-2.2.1-02 Generate acceptance criteria from a wireframe
Activity: Acceptance criteria from a wireframe
Solution: Acceptance criteria from a wireframe
VID-2.2.1-03 Refine acceptance criteria through prompt chaining
Activity: Analyze and refine a user story
Solution: Analyze and refine a user story
VID-2.2.1-04 Review analysis outputs and resolve ambiguity