The rapid integration of AI components into software systems introduces new complexities for requirements engineering (RE). Despite the widespread adoption of AI, few public datasets of requirements and their associated trace links exist for these systems. This lack of available data hinders progress in RE4AI, limiting both the evaluation of existing techniques and the development of new approaches that address the unique challenges of AI. To help bridge this gap, we present a dataset from a real-world Generative AI application consisting of source code written in multiple languages and a diverse hierarchy of software artifacts, including features, functional and non-functional requirements, and design constraints. The dataset also provides manually constructed trace links between these natural language artifacts, with end-to-end traceability to code for a subset of AI-related features. We outline potential research applications of this dataset and describe four challenges encountered in specifying and maintaining requirements for the AI components. Additionally, we present our approaches for addressing these challenges as reusable traceability patterns. By releasing this dataset, we aim to provide a foundation for advancing further research in requirements engineering for AI-based systems.