Towards a Meta-Model for Integrating Knowledge Graph Extraction Into LLM-Enhanced Engineering Design

Abstract

Generative AI (GenAI) tools, specifically LLMs, have seen an explosion in use across professional fields in recent years. Researchers have explored integrating GenAI tools into Multi Agent Systems (MAS) for automated engineering design pipelines. However, engineering subfields contain domain knowledge which includes tacit expertise that is often missing from LLM training data. Applying LLMs without tacit knowledge to domain-specific tasks increases the risk of injecting false or misguided information into the design process. These risks are even further amplified when using autonomous Agentic LLMs that can act independently. Knowledge Graphs (KGs) can improve LLM behavior, but manual domain KG creation is resource intensive. Automating KG creation via Knowledge Graph Extraction (KGE) using LLMs is becoming more common, but its implementation is underexplored in engineering design pipelines. This paper proposes a meta model to explore the tradespace behind LLM-based options for MAS engineering design workflows.