ISTQB Certified Tester - Testing with Generative AI + Exam [ISTQB-CT-GENAI]

OVERVIEW

The ISTQB CT-GenAI training provides you with the knowledge and techniques to use GenAI tools and Large Language Models (LLMs) responsibly in software testing. In two days, you will learn how to enhance test processes with AI, manage risks, and integrate this technology into test infrastructures, while respecting ethics, privacy, and quality.

OBJECTIVES

After completing this course you should be able to:   

• Understand the fundamental concepts, capabilities, and limitations of GenAI and LLMs in software testing.
• Develop and refine prompts for effective use of GenAI in test analysis, design, automation, and reporting.
• Identify and mitigate risks (hallucinations, reasoning errors, biases, privacy, security, environmental impact) associated with GenAI in testing.
• Explain and experiment with LLM-powered test infrastructure, including Retrieval-Augmented Generation and LLMOps.
• Contribute to the adoption and integration of GenAI in test organizations, including change management and skills development.

AUDIENCE

This training is intended for testers, test analysts, test automation specialists, test managers, and other QA professionals who want to learn how to use Generative AI responsibly. The ISTQB Foundation Level (CTFL) certificate is required; programming knowledge is not necessary, but an affinity for AI or technological change is helpful.

CERTIFICATION

Recommended as preparation for the following exam:

• ISTQB® Certified Tester - Testing with Generative AI (CT-GenAI)

CONTENT

1. Introduction to Generative AI for Software Testing

• Core concepts of GenAI, LLMs, tokenization, embeddings, multimodal models
• Applications of AI in the testing lifecycle

2. Prompt Engineering for Effective Software Testing

• Structured prompts, chaining, few-shot and meta prompting
• Applying prompts to test design and automation

3. Managing Risks of Generative AI in Software Testing

• Addressing hallucinations, bias, non-determinism, and security/privacy issues
• Ethical and regulatory considerations
• Assessing environmental impact of AI

4. LLM-Powered Test Infrastructure

• Retrieval-Augmented Generation (RAG), fine-tuning, and LLMOps
• Using AI agents for automated test processes

5. Deploying and Integrating GenAI in Test Organisations

• Creating an organizational roadmap for GenAI adoption
• Building AI testing capabilities in teams
• Adapting processes for AI-enabled environments