Your certification guide · Specialist Level
Learn how to apply generative AI and large language models throughout software testing. Build effective prompts, assess generated results, manage AI-related risks, and understand the infrastructure and organizational changes needed to adopt GenAI in testing.
The exam uses weighted questions: points are not the same as the number of correct answers. Candidates taking the exam in a non-native language may receive 25% additional time (75 minutes total). Confirm eligibility with your exam provider.
Testers, test analysts, automation engineers, test managers, developers, and quality professionals using or adopting generative AI for testing.
The ISTQB Certified Tester Foundation Level (CTFL) certificate is required. Prepare through self-study or accredited training and practise prompting LLMs on testing tasks.
The syllabus at a glance
A concise guide to the certification content in syllabus v1.1.
Introduces generative AI and LLM foundations, including tokenization, context windows, model types, and multimodal capabilities. Explores how LLM capabilities support test tasks and distinguishes AI chatbots from LLM-powered testing applications.
Key topicsLLM foundations · Tokens and context · Multimodal models · Testing applications
Explains how to structure prompts and choose prompting techniques for test analysis, design, implementation, regression testing, monitoring, and control. Covers evaluating generated results with task-specific metrics and iteratively refining prompts.
Key topicsPrompt structure · Prompting techniques · Test tasks · Output evaluation
Covers identifying and mitigating hallucinations, reasoning errors, bias, and non-deterministic output. Addresses privacy and security risks, environmental impact, and the role of regulations, standards, and best-practice frameworks.
Key topicsHallucinations · Bias · Privacy and security · Environmental impact
Explores architectural components of LLM-powered test infrastructure, retrieval-augmented generation, and agents that automate testing processes. Introduces fine-tuning for testing tasks and LLMOps for deploying, managing, and monitoring models.
Key topicsRAG · LLM agents · Fine-tuning · LLMOps
Explains a structured adoption roadmap, model selection, and the risks of unmanaged shadow AI. Covers GenAI strategy, team skills, capability development, and changes to testing processes as organizations integrate AI.
Key topicsAdoption roadmap · Shadow AI · Model selection · Organizational change
Training times are syllabus allocations for accredited courses, not exam section durations or personal study recommendations. Total: 13 hours 35 minutes. Reference material and appendices follow the core chapters in the syllabus.
Study from the source
Check the certification page for exam information, study the syllabus, and use official sample questions and answers to practise.
Official sample answers ↗ · All official downloads ↗
For CT-GenAI sample questions and answers, open the certification page and scroll to “Download Materials” → “Sample Exams”. The syllabus link opens the official download page.
Independent study guide based on the ISTQB CT-GenAI syllabus v1.1. Syllabus source and copyright: International Software Testing Qualifications Board (ISTQB®) and its authors. This page is not affiliated with or endorsed by ISTQB. Links checked on 7 October 2026.
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