  Changes and updates in the requirement artifacts, which can be frequent in
the automotive domain, are a challenge for SafetyOps. Large Language Models
(LLMs), with their impressive natural language understanding and generating
capabilities, can play a key role in automatically refining and decomposing
requirements after each update. In this study, we propose a prototype of a
pipeline of prompts and LLMs that receives an item definition and outputs
solutions in the form of safety requirements. This pipeline also performs a
review of the requirement dataset and identifies redundant or contradictory
requirements. We first identified the necessary characteristics for performing
HARA and then defined tests to assess an LLM's capability in meeting these
criteria. We used design science with multiple iterations and let experts from
different companies evaluate each cycle quantitatively and qualitatively.
Finally, the prototype was implemented at a case company and the responsible
team evaluated its efficiency.
