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ISTQB® Certified Tester Specialist Level – Testing with Generative AI (CT-GenAI)

ISTQB® CT Testing with Generative AI (CT-GenAI)

Course Overview

The ISTQB® Certified Tester Specialist Level – Testing with Generative AI (CT-GenAI) certification is designed for software testing professionals who want to effectively leverage Generative AI and Large Language Models (LLMs) throughout the software testing lifecycle. The course provides both practical and strategic guidance on applying AI to improve test analysis, test design, automation, reporting, and quality assurance activities.

Delegates will gain a solid understanding of Generative AI concepts, prompt engineering techniques, GenAI risks and mitigation strategies, LLM-powered testing infrastructures, and effective approaches for integrating AI into modern testing organisations. Through practical hands-on exercises and real-world examples, learners will discover how AI can enhance productivity while maintaining quality, governance, security, and compliance standards.

Who Should Attend?

This course is ideal for:

  • Software Testers
  • Test Analysts
  • Test Automation Engineers
  • QA Engineers
  • Test Managers
  • Software Developers
  • User Acceptance Testers
  • Business Analysts
  • Quality Managers
  • Project Managers
  • IT Directors
  • Management Consultants
  • Anyone involved in applying Generative AI within software testing activities
  • Testing professionals looking to develop practical AI testing skills and achieve an internationally recognised ISTQB® certification

What You Will Learn

Upon completion of this course, delegates will be able to:

  • Understand the fundamental concepts, capabilities and limitations of Generative AI
  • Apply Large Language Models (LLMs) to software testing activities
  • Develop effective prompt engineering skills for testing tasks
  • Create and refine prompts using industry-recognised prompting techniques
  • Apply Generative AI to test analysis, design, implementation and automation activities
  • Evaluate and improve AI-generated testing outputs
  • Identify and mitigate hallucinations, reasoning errors and bias in AI-generated results
  • Understand data privacy, security and compliance considerations when using AI
  • Explore Retrieval-Augmented Generation (RAG), LLM-powered agents and AI testing architectures
  • Understand LLMOps and fine-tuning approaches for testing environments
  • Contribute to Generative AI adoption strategies within testing organisations
  • Build practical skills for using AI responsibly and effectively throughout the testing lifecycle

Course Modules

Module 1: Introduction to Generative AI for Software Testing

Establish a solid foundation in Generative AI and understand how it can be applied to modern software testing practices.

Topics include:

  • AI fundamentals and evolution
  • Symbolic AI, Machine Learning and Deep Learning
  • Generative AI and Large Language Models (LLMs)
  • Foundation, Instruction-Tuned and Reasoning Models
  • Tokenisation and embeddings
  • Context windows and model limitations
  • Multimodal AI and vision-language models
  • AI chatbots versus LLM-powered testing tools
  • Key AI capabilities for software testing
  • Practical uses of AI across the testing lifecycle

Module 2: Prompt Engineering for Effective Software Testing

Learn how to communicate effectively with AI models to obtain accurate, reliable and valuable testing outputs.

Topics include:

  • Principles of effective prompt design
  • Structured prompt development
  • Roles, context, instructions and constraints
  • System prompts and user prompts
  • Prompt chaining
  • Few-shot and one-shot prompting
  • Meta prompting
  • AI-assisted test analysis
  • Generating acceptance criteria using AI
  • Test case generation with AI
  • Gherkin and behaviour-driven testing support
  • AI-assisted automated regression testing
  • AI-assisted test monitoring and reporting
  • Selecting appropriate prompting techniques
  • Evaluating and refining prompts

Module 3: Managing Risks of Generative AI in Software Testing

Understand the challenges, limitations and governance requirements associated with AI-driven testing.

Topics include:

  • Hallucinations in Generative AI
  • Reasoning errors and bias
  • Detecting AI-generated inaccuracies
  • Human review and validation techniques
  • Mitigation strategies for AI risks
  • Managing non-deterministic AI behaviour
  • Data privacy considerations
  • Security risks and vulnerabilities
  • Data protection and GDPR considerations
  • AI attack vectors and threat scenarios
  • Secure use of AI in testing
  • Environmental impact of AI
  • Energy consumption and sustainability
  • AI regulations and standards
  • EU AI Act
  • ISO/IEC AI standards
  • NIST AI Risk Management Framework

Module 4: LLM-Powered Test Infrastructure for Software Testing

Explore how AI-powered testing ecosystems are designed, deployed and managed.

Topics include:

  • LLM-powered testing architectures
  • AI-enabled test infrastructure
  • Front-end and back-end AI testing components
  • Retrieval-Augmented Generation (RAG)
  • Vector databases and embeddings
  • AI-powered knowledge retrieval
  • LLM-powered agents
  • Agent-based test automation
  • Autonomous and semi-autonomous testing agents
  • Fine-tuning language models
  • Small Language Models (SLMs)
  • LLMOps concepts and practices
  • Deploying and maintaining AI-powered testing solutions

Module 5: Deploying and Integrating Generative AI in Test Organisations

Learn how organisations can successfully adopt, govern and scale Generative AI within testing teams.

Topics include:

  • Building a Generative AI strategy
  • Risks of Shadow AI
  • Establishing AI governance
  • Selecting appropriate LLMs and SLMs
  • Cost considerations and ROI
  • AI capability development
  • AI skills for testing professionals
  • Building AI-enabled test teams
  • Organisational change management
  • Roadmaps for AI adoption
  • Scaling AI across testing functions
  • Evolving roles for testers and test managers
  • Establishing AI best practices
  • Continuous improvement and AI maturity

Examination

The course prepares delegates for the ISTQB® Certified Tester Specialist Level – Testing with Generative AI (CT-GenAI) examination.

  • Multiple-choice examination
  • Internationally recognised certification
  • Based on the official ISTQB® CT-GenAI syllabus
  • Specialist-level qualification
  • Covers both theory and practical application of AI in software testing
  • Suitable for professionals seeking to develop AI testing skills and credentials
  • Builds upon existing software testing knowledge and practices
  • Provides practical, immediately applicable skills for modern testing environments

Prerequisite: Delegates must hold the ISTQB® Certified Tester Foundation Level (CTFL) certification before taking the CT-GenAI examination.

Course Duration

2 Days Instructor-Led Training

Available as:

  • Classroom
  • Virtual Classroom
  • On-site Corporate Delivery
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