  Generative AI is increasingly important in software engineering, including
safety engineering, where its use ensures that software does not cause harm to
people. This also leads to high quality requirements for generative AI.
Therefore, the simplistic use of Large Language Models (LLMs) alone will not
meet these quality demands. It is crucial to develop more advanced and
sophisticated approaches that can effectively address the complexities and
safety concerns of software systems. Ultimately, humans must understand and
take responsibility for the suggestions provided by generative AI to ensure
system safety. To this end, we present an efficient, hybrid strategy to
leverage LLMs for safety analysis and Human-AI codesign. In particular, we
develop a customized LLM agent that uses elements of prompt engineering,
heuristic reasoning, and retrieval-augmented generation to solve tasks
associated with predefined safety concepts, in interaction with a system model
graph. The reasoning is guided by a cascade of micro-decisions that help
preserve structured information. We further suggest a graph verbalization which
acts as an intermediate representation of the system model to facilitate
LLM-graph interactions. Selected pairs of prompts and responses relevant for
safety analytics illustrate our method for the use case of a simplified
automated driving system.
