Now that we’ve established a mindset that promotes a human-led approach, we can begin to build our techniques for using LLMs across a range of testing activities. In this part, we’ll not only focus on ways in which LLMs can be employed to enhance specific tasks within testing through the use of prompt engineering and AI agents, but also learn how to identify tasks in which LLMs are beneficial. Focusing on this later skill is fundamental for success, and we’ll learn why this is so by comparing examples of LLM use, focusing on broad, generalized tasks versus small, targeted tasks. By establishing clear goals and outputs with smaller tasks, we’ll be better equipped for developing more valuable prompts that can be tweaked and improved using prompt-engineering techniques. All this can be utilized in an AI agent to create assistant tools that can support specific tasks. So, let’s explore how task identification and prompt engineering can be applied to a wide range of testing tasks, such as development, automation, analysis, and exploration, to enhance the already valuable work we do to improve our product’s quality.