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Unlock the Future of Software Testing with Generative AI

Join the next generation of testing professionals. Our comprehensive training prepares you for the ISTQB CT-GenAI exam, equipping you with essential skills in prompt engineering, AI risk management, and LLM-powered test infrastructure

  • Official ISTQB CT-GenAI Syllabus Coverage
  • Hands-on Prompt Engineering Exercises
  • Understand AI Risks: Hallucinations, Bias, Security
  • Learn LLM-Powered Test Automation
  • Expert Instructors & Practical Insights

Why Our ISTQB CT-GenAI Training?

Expert Instructors:

Learn from industry veterans with deep Generative AI and testing expertise.

Official Syllabus Alignment

100% aligned with the latest ISTQB CT-GenAI syllabus for guaranteed exam readiness.

Hands-on Practice

Practical exercises in prompt engineering and AI tool usage.

Flexible Learning

Self-paced online modules, live virtual sessions, or corporate training.

Frequently Asked Questions



This certification is designed for anyone looking to leverage Generative AI in their software testing roles, including:

  • Software Testers & QA Engineers
  • Test Analysts & Test Managers
  • Test Automation Engineers
  • Software Developers
  • Project Managers & IT Directors
  • Anyone aspiring to integrate AI into their testing practices.

The ISTQB Certified Tester - Testing with Generative AI (CT-GenAI) certification validates your knowledge and skills in applying Generative AI (GenAI) techniques to software testing. It covers essential topics like prompt engineering for test tasks, managing risks associated with AI in testing, and understanding LLM-powered test infrastructure. This certification helps you stay at the forefront of AI innovation in the QA industry.

To be eligible to take the ISTQB CT-GenAI certification exam, candidates must already hold the ISTQB® Foundation Level certificate. This ensures a foundational understanding of software testing principles before delving into the specialized area of Generative AI in testing.

Our comprehensive training program for the ISTQB CT-GenAI certification is designed to cover all syllabus topics, with an estimated total instruction time of 13.6 hours. 

This can be completed at your own pace through self-study modules or within structured live virtual sessions, depending on your chosen learning format.

Absolutely! Our course is meticulously designed to align 100% with the official ISTQB CT-GenAI syllabus. We cover all learning objectives (K1, K2, K3) and incorporate hands-on exercises (H0, H1, H2) to provide practical experience. Our goal is to equip you with all the necessary knowledge and skills to confidently pass the CT-GenAI examination.

The training includes practical, hands-on exercises to solidify your understanding and application of GenAI in testing. You'll gain experience in:

  • Reviewing and executing prompts with multimodal LLMs.
  • Creating structured prompts to generate acceptance criteria from wireframes.
  • Generating test conditions from ambiguous requirements.
  • Developing prompts for test cases, test procedures, and test data.
  • Generating test automation scripts and test oracles.
  • Evaluating and refining prompts iteratively.
  • Identifying hallucinations, reasoning errors, and biases in LLM output.

The ISTQB CT-GenAI certification offers significant career advantages, including:

  • Enhanced Employability: Positions you as an expert in a rapidly evolving and highly sought-after field.
  • Future-Proofing Your Skills: Ensures you are equipped for the future of software testing, which increasingly relies on AI.
  • Increased Earning Potential: Certified professionals often command higher salaries.
  • Industry Recognition: Demonstrates your commitment to continuous learning and your expertise in leveraging AI for quality assurance.
  • Innovation Catalyst: Enables you to drive efficiency and innovation within your testing processes and organization.

25 Hands-On Exercises Using Best-in-Class AI tools

What You'll Learn: ISTQB CT-GenAI Course Overview

A concise overview of the 5 main chapters, designed to provide a deep dive into applying Generative AI to software testing, from foundational concepts to advanced prompt engineering and strategic deployment.

Chapter 1: Introduction to Generative AI for Software Testing

  • 1.1 Generative AI Foundations and Key Concepts
  • Hands-on Exercise: Practice tokenization and token count evaluation when using an LLM for a software test task
  • Hands-on Exercise: Write and execute a prompt for a multimodal LLM using both textual and image inputs for a software test task
  • 1.2 Leveraging Generative AI in Software Testing: Core Principles
  • Practice Exam - Chapter 1


Chapter 2: Prompt Engineering for Effective Software Testing 

  • 2.1 Effective Prompt Development
  • Hands-on Exercise:  Observe several given prompts for software test tasks, identifying the components of role, context, instruction, input data, constraints and output format in each
  • Hands-on Exercise:  Observe demonstrations of prompt chaining, few-shot prompting, and metaprompting applied to software test tasks
  • Hands-on Exercise:  Identify which prompt engineering techniques are being used in given examples
  • 2.2 Applying Prompt Engineering Techniques to Software Test Tasks
  • Hands-on Exercise: Practice creating structured multimodal prompts to generate acceptance criteria for a user story based on a GUI wireframe
  • Hands-on Exercise: Practice prompt chaining and human verification to progressively analyze a given user story and refine acceptance criteria
  • Hands-on Exercise: Practice functional test case generation from user stories with generative AI using prompt chaining, structured prompts and meta-prompting
  • Hands-on Exercise: Use few-shot prompting technique to generate Gherkin style test conditions and test cases from user stories
  • Hands-on Exercise: Use prompt chaining to prioritize test cases within a given test suite, taking into account their specific priorities and dependencies
  • Hands-on Exercise: Practice few-shot prompting to create and manage keyword-driven test scripts
  • Hands-on Exercise: Practice structured prompt engineering for test report analysis
  • Hands-on Exercise: Observe test monitoring metrics prepared by generative AI from test data
  • Hands-on Exercise: Select and apply context-appropriate prompting techniques for a given test task
  • 2.3 Evaluate Generative AI Results and Refine Prompts for Software Test tasks
  • Hands-on Exercise: Observe how metrics can be used for evaluating the result of generative AI ona test task
  • Hands-on Exercise: Evaluate and optimize a prompt for a given test task
  • Practice Exam - Chapter 2


Chapter 3: Managing Risks of Generative AI in Software Testing

  • 3.1 Hallucinations, Reasoning Errors and Biases
  • Hands-on Exercise: Experiment with hallucinations in testing with GenAI
  • Hands-on Exercise: Experiment with reasoning errors in testing with GenAI
  • 3.2 Data Privacy and Security Risks of Generative AI in Software Testing
  • Hands-on Exercise: Recognize data privacy and security risks in a given Generative AI for testing case study
  • 3.3 Energy Consumption and Environmental Impact of Generative AI in Software Testing
  • Hands-on Exercise: Use a simulator to calculate the energy and CO₂ emissions for given test tasks with Generative AI
  • 3.4 AI Regulations, Standards, and Best Practice Frameworks
  • Practice Exam - Chapter 3


Chapter 4: LLM-Powered Test Infrastructure for Software Testing

  • 4.1 Architectural Approaches for LLM-Powered Test Infrastructure
  • Hands-on Exercise: Experiment with Retrieval-Augmented Generation for a given test task
  • Hands-on Exercise: Observe how an LLM-powered agent assists in automating a repetitive test task
  • 4.2 Fine-Tuning and LLMOps: Operationalizing Generative AI for Software Testing
  • Hands-on Exercise: Observe an example of a fine-tuning process for a given test task and language model
  • Practice Exam - Chapter 4


Chapter 5: Deploying and Integrating Generative AI in Test organizations

  • 5.1 Roadmap for the Adoption of Generative AI in Software Testing
  • Hands-on Exercise: Estimate the recurring costs of using Generative AI for a given test task
  • 5.2 Manage Change when Adopting Generative AI for Software Testing
  • Practice Exam - Chapter 5

View Full Syllabus