As modern technical systems grow in complexity, ensuring the quality of these systems during early development phases becomes more challenging. This is particularly evident in the development of modern passenger vehicles, where non-functional requirements (NFRs) play a critical role in ensuring that a vehicle operates according to specified standards and expectations, especially across different vehicle configurations and environmental conditions. The introduction of Artificial Intelligence (AI) in automotive engineering has transformed the approach to vehicle system design and development. This paper presents a pipeline for analyzing and generating NFRs for vehicle systems using generative AI-based methods. The pipeline categorizes NFRs, explores their interdependencies with vehicle configurations and environmental conditions, and addresses the completeness of NFRs in relation to specific vehicle use cases. The paper focuses on selecting appropriate NFR types for various use cases, taking into account diverse configurations and environmental factors. Examples of NFRs with varying parameters are provided for an electric vehicle under development at a leading car manufacturer, illustrating the benefit as well as the challenges of applying generative AI to automotive engineering.