专家心：能源领域专家知识保存的检索增强架构
摘要
主题领域专家离职会导致隐性知识不可逆损失，传统文档实践往往未能捕获。本文提出Expert Mind系统，利用检索增强生成（RAG）、大型语言模型（LLM）和多模态捕获技术，保存、结构化并使其可查询的组织知识持有者的深层专业知识。drawing on the specific context of the energy sector, where decades of operational experience risk being lost to an aging workforce, we describe the system architecture, processing pipeline, ethical framework, and evaluation methodology. The proposed system addresses the knowledge elicitation problem through structured interviews, think-aloud sessions, and text corpus ingestion, which are subsequently embedded into a vector store and queried through a conversational interface. Preliminary design considerations suggest Expert Mind can significantly reduce knowledge transfer latency and improve onboarding efficiency. Ethical dimensions including informed consent, intellectual property, and the right to erasure are addressed as first-class design constraints.
引用
@article{arxiv.2603.14541,
title = {Expert Mind: A Retrieval-Augmented Architecture for Expert Knowledge Preservation in the Energy Sector},
author = {Diego Ezequiel Cervera},
journal= {arXiv preprint arXiv:2603.14541},
year = {2026}
}
备注
6 pages, 1 figure, conceptual architecture paper on retrieval-augmented expert knowledge systems