This qualification is designed for anyone working with generative AI in software testing—whether you're a tester, test analyst, automation engineer, test manager, UAT specialist, or developer. It's also a great fit for professionals who want a strong grasp of GenAI in testing, including project managers, quality leads, development managers, business analysts, IT directors, and consultants.

This course builds on the Foundation level to equip testing and quality engineering professionals with practical expertise in applying Large Language Models (LLMs) and generative AI across the entire testing lifecycle—from requirements analysis and test design to automation, reporting, and continuous improvement.

The certification covers GenAI fundamentals and develops hands-on skills through real-world prompt engineering techniques and applied testing scenarios. You will learn to integrate GenAI responsibly while addressing critical risks such as hallucinations, bias, security, privacy, and environmental impact.

Prerequisites

Before taking the CT-GenAI exam, you must be certified ISTQB® Certified Tester Foundation Level (CTFL).

• Understand the fundamental concepts, capabilities, and limitations of generative AI
• Develop practical skills in prompting large language models for software testing
• Gain insight into the risks and mitigations of using generative AI for software testing
• Gain insight into the applications of generative AI solutions for software testing
• Contribute effectively to the definition and implementation of a generative AI strategy and roadmap for software testing within an organization

Yes:

• The examination consists of a one-hour exam with 40 multiple-choice questions.
• It will be a ‘closed book’ exam – no notes or reference materials are allowed.
• Duration: 60 minutes (or 75 minutes for candidates taking the exam in a non-native language).
• Pass mark: 65% (26 out of 40 correct answers).
• The exam fee is included in the course price.

ISTQB Certified Tester Specialist Level Testing with Generative AI (CT-GenAI) (a two-day course)

Course Content

Introduction to Generative AI for Software Testing

• GenAI Foundations and Key Concepts
• Leveraging GenAI in Software Testing: Core Principles

Prompt Engineering for Effective Software Testing

• Effective Prompt Development
• Applying Prompt Engineering Techniques
• Evaulate GenAI Results and Refine Prompts

Managing Risks of Generative AI in Software Testing

• Hallucinations, Reasoning Errors and Biases
• Data Privacy and Security Risks
• Energy Consumption and Environmental Impact of GenAI
• AI Regulations, Standards and Best Practice Frameworks

LLM-Powered Test Infrastructure for Software Testing

• Architectural Approaches for LLM-Powered Testing Solutions
• Fine-Tuning and LLMOps: Operationalizing GenAI

Deploying and Integrating Generative AI in Test Organizations

• Roadmap for Adoption of GenAI
• Manage Change when Adopting GenAI