From Prompting to Quality: A Full-Day Workshop on Generative AI for Testers
Speaker
Target audience
Basic (no prior knowledge)
Description
Generative AI is changing how software teams think, work, and deliver value. For testers and quality professionals, this shift creates both pressure and opportunity. Many teams already use AI informally, but often without a structured approach, clear quality criteria, or a shared understanding of risks and limits. This workshop gives participants a practical and grounded way to use Generative AI in testing based on the ISTQB CT-GenAI syllabus.
In this full-day interactive workshop, participants will learn how Generative AI can support key testing activities such as test analysis, test design, review support, idea generation, risk identification, and communication. The workshop does not treat AI as magic. Instead, it focuses on where AI adds value, where human judgment stays essential, and how to work with AI responsibly in a quality-focused environment.
The session combines short theory inputs, guided demonstrations, structured prompting exercises, peer discussion, and hands-on practice. Participants will work on realistic testing scenarios and learn how to create better prompts, assess AI outputs critically, reduce hallucination risks, and improve the usefulness of AI-generated results. The workshop also addresses limitations, governance concerns, and practical rules for safe adoption in testing contexts.
The content aligns with the conference theme “Shifting Skills, Shaping Quality Culture” by showing how testers need to expand their skill set without losing core testing principles. The goal is not to replace testers with AI, but to strengthen critical thinking, improve productivity, and help quality professionals work more effectively in modern delivery environments.
Attendees will leave with practical techniques, reusable prompt patterns, and a clear understanding of how to apply Generative AI in testing in a responsible and value-oriented way.
You will learn
To explain where Generative AI supports software testing effectively and where human expertise remains essential
To apply structured prompting techniques for testing-related tasks such as test design, requirement review, and risk exploration
To evaluate AI-generated outputs critically for relevance, completeness, correctness, and risk
To identify common limitations and risks of Generative AI in testing, including hallucinations, bias, confidentiality, and overreliance
To use practical prompt patterns and quality checks to improve day-to-day testing work