Imagine a world where software development is as easy as conversing with an AI assistant. You describe what you want, and the AI builds it – almost like magic. But what if the AI misunderstands your instructions? This is the current challenge with large language models (LLMs) in requirements engineering, the crucial process of defining what a software system should do. LLMs are incredibly powerful at generating code from natural language instructions, which we call prompts. However, these prompts must be clear, specific, and well-structured for the LLM to work effectively. Research by Andreas Vogelsang at the University of Cologne highlights the importance of precise prompts for shaping software development with generative LLMs. It's not enough just to tell the AI what to do; you have to tell it in a way it understands. This is where requirements engineering comes in. Vogelsang's work explores how traditional RE methods can be applied to prompt engineering, creating a synergy between human understanding and AI capabilities. Think of it like writing an effective set of instructions: You need to know the right language, level of detail, and structure to get the desired outcome. Similarly, prompts need to be crafted to maximize the LLM's ability to deliver accurate and reliable code. Vogelsang's research also touches upon important considerations for evaluating LLM output, addressing the challenges of conversational flow, human trust, and feedback mechanisms. Moreover, the research goes beyond individual prompts and delves into a broader organizational context: How do we ensure all stakeholder perspectives are included in prompts? How do we manage prompts as critical development artifacts throughout the project lifecycle? The integration of generative LLMs into software development is still in its early stages, but it's clear that the future depends on mastering the art of precise prompts. Vogelsang's work offers valuable insights into how we can harness the power of LLMs to build better software, faster, and more efficiently, ultimately transforming how software requirements are expressed and managed.