  Representation of systems using the SAPPhIRE model of causality can be an
inspirational stimulus in design. However, creating a SAPPhIRE model of a
technical or a natural system requires sourcing technical knowledge from
multiple technical documents regarding how the system works. This research
investigates how to generate technical content accurately relevant to the
SAPPhIRE model of causality using a Large Language Model, also called LLM. This
paper, which is the first part of the two-part research, presents a method for
hallucination suppression using Retrieval Augmented Generating with LLM to
generate technical content supported by the scientific information relevant to
a SAPPhIRE con-struct. The result from this research shows that the selection
of reference knowledge used in providing context to the LLM for generating the
technical content is very important. The outcome of this research is used to
build a software support tool to generate the SAPPhIRE model of a given
technical system.
