Retrieval-Augmented Generation Applications and Development in the Field of Electronic Test Instruments

Abstract

The rapid development of artificial intelligence (AI), especially the fast iteration and widespread deployment of large language models (LLMs), has ushered in a new paradigm for the intelligent augmentation of electronic test instruments and automatic test systems (ATS). By combining the powerful reasoning and generative capabilities of LLMs with the timeliness and accuracy of external knowledge bases, retrieval-augmented generation (RAG) has emerged as a highly promising approach to addressing knowledge-intensive challenges in the operation and maintenance of electronic test instruments. This paper presents a comprehensive review of RAG and its applications in the field of electronic test instruments. First, the basic architecture of RAG is reviewed, including retrieval mechanisms, knowledge-base construction, and generative models. Then, considering the operational characteristics of electronic test instruments and automatic test systems, key knowledge-intensive tasks are analyzed, such as fault diagnosis, test configuration, and process planning, followed by a discussion of representative RAG-based applications, including intelligent fault diagnosis systems, adaptive test-parameter configuration, and interactive question-answering assistants. In addition, practical considerations for engineering implementation are discussed with respect to knowledge-base construction and effectiveness evaluation. Finally, open issues and future research directions are outlined, including multimodal RAG, federated learning for distributed knowledge bases, and edge deployment. This review aims to provide researchers and practitioners in relevant industries with a comprehensive understanding of the current status and development prospects of RAG technologies in electronic test instruments.