The adoption of large language models in safety-critical system engineering is constrained by trustworthiness, traceability, and alignment with established verification practices. We propose workflow-level design principles for trustworthy GenAI integration and demonstrate them in an end-to-end automotive pipeline, from requirement delta identification to SysML v2 architecture update and re-testing. First, we show that monolithic ("big-bang") prompting misses critical changes in large specifications, while section-wise decomposition with diversity sampling and lightweight NLP sanity checks improves completeness and correctness. Then, we propagate requirement deltas into SysML v2 models and validate updates via compilation and static analysis. Additionally, we ensure traceable regression testing by generating test cases through explicit mappings from specification variables to architectural ports and states, providing practical safeguards for GenAI used in safety-critical automotive engineering.

翻译：在安全关键系统工程中采用大语言模型受到可信性、可追溯性以及与既定验证实践对齐的制约。我们提出了用于可信生成式人工智能集成的工作流级设计原则，并在从需求差异识别到SysML v2架构更新与再测试的端到端汽车工程流水线中进行了验证。首先，我们证明整体式（"大爆炸"）提示方法会遗漏大型规范中的关键变更，而采用分节分解结合多样性采样与轻量级自然语言处理合理性检查的方法，能提升完整性与正确性。随后，我们将需求差异传递至SysML v2模型中，并通过编译与静态分析验证更新。此外，我们通过建立从规范变量到架构端口及状态的显式映射来生成测试用例，从而确保可追溯的回归测试，为安全关键汽车工程中使用的生成式人工智能提供了实用的保障措施。