LLMs with retrieval-augmented generation can automatically build executable functional models from technical docs, enabling scalable diagnostic reasoning for complex systems that traditionally required manual expert work.
This paper automates the construction of Dynamic Master Logic models—hierarchical diagrams showing how system failures propagate—by converting technical documentation into knowledge graphs using AI. The approach enables faster analysis of complex systems like nuclear reactors for safety and diagnostics without requiring manual expert interpretation.