With the advent of generative LLMs and their advanced code generation capabilities, some people already envision the end of traditional software engineering because LLMs may be able to produce high-quality code just based on requirements a domain expert feeds into the system. The feasibility of this vision may be assessed by understanding how developers currently incorporate requirements when using LLMs for code generation - a topic that remains largely unexplored. We have interviewed 18 practitioners from 14 companies to understand how they (re)use information from requirements and other design artifacts to feed LLMs when generating code. Based on our findings, we propose a theory that explains the processes developers employ and the artifacts they rely on. Our theory suggests that requirements, as typically documented, are too abstract for direct input into LLMs. Instead, they must first be manually decomposed into programming tasks, which are then enriched with design decisions and architectural constraints before being used in prompts. Our study highlights that fundamental RE work is still necessary when LLMs are used to generate code. Our theory is important for contextualizing scientific approaches to automating requirements-centric SE tasks.