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The integration of generative AI in software testing is not just a leap forward, but a transformative journey that demands both enthusiasm and critical thinking. In 2022, the world as we know it changed forever with the launch of OpenAI’s AI chatbot, ChatGPT. Soon after, Google and Microsoft introduced their own LLM tools, followed by many other companies. This brought generative AI into the public consciousness. The shock that the growth of these tools has sent through the software engineering community has given a jolt to the heart of the tech industry and set the task of quick take-up of tools and skills. This development has been thrilling at best and challenging at worst for software engineering teams worldwide. Roles tasked with testing often feel the seismic shifts of changes in tech, and this time is no different as engineering teams attempt to juggle advanced automation adoption with the additional herculean task of integrating LLMs. From a testing perspective, this requires that we stay informed and ready in terms of skills. Automation has now become synonymous with testing platforms using LLMs. Following a holistic approach that is human-centered and brings different approaches to testing platforms is still important, and Software Testing with Generative AI encourages this. It reminds us that while LLMs offer powerful support, human judgment and understanding remain at the core of effective testing. Today, there are numerous courses that cover AI testing, but for someone trying to decide which course to take and how to implement what they have learned, this can be overwhelming. The beauty of Mark’s book is that, while it takes big concepts around LLMs and explains them, it also includes worked test examples the user can put into action straightaway. It is also not just about learning every tool, but giving thought pieces around why we use them and what to consider in terms of strategy and planning for products we are trying to build and test. This book is a detailed guide to integrating generative AI into the testing process thoughtfully and effectively. It offers practical insights and emphasizes the need for balance and critical thinking when adopting AI tools, particularly in the context of automation. In essence, Software Testing with Generative AI is a must-read for anyone who wants to understand AI-assisted testing from a technical perspective. It equips readers with the reference knowledge and examples needed to approach their projects. I am excited to see how this book will help engineers with approaches and best practices. I highly recommend it to anyone looking to stay ahead of the curve.