  Engineering safety-critical systems such as medical devices and digital
health intervention systems is complex, where long-term engagement with
subject-matter experts (SMEs) is needed to capture the systems' expected
behaviour. In this paper, we present a novel approach that leverages Large
Language Models (LLMs), such as GPT-3.5 and GPT-4, as a potential world model
to accelerate the engineering of software systems. This approach involves using
LLMs to generate logic rules, which can then be reviewed and informed by SMEs
before deployment. We evaluate our approach using a medical rule set, created
from the pandemic intervention monitoring system in collaboration with medical
professionals during COVID-19. Our experiments show that 1) LLMs have a world
model that bootstraps implementation, 2) LLMs generated less number of rules
compared to experts, and 3) LLMs do not have the capacity to generate
thresholds for each rule. Our work shows how LLMs augment the requirements'
elicitation process by providing access to a world model for domains.
