This research introduces the first comprehensive framework to evaluate how well Large Language Models (LLMs) understand and perform in the specialized Test & Measurement domain.

• Developed TMIQ benchmark with 61 challenging domain-specific questions
• Tested GPT-4, Claude, Llama-2, and other leading LLMs on specialized engineering tasks
• Revealed significant performance gaps between different models in understanding precision measurement concepts
• Identified test automation, equipment control, and SCPI command understanding as critical areas for improvement

This research matters for engineering teams because it helps identify which LLMs are best suited for integration into precision testing workflows, potentially accelerating automation while maintaining accuracy standards.

TMIQ: Quantifying Test and Measurement Domain Intelligence in Large Language Models