The departure of subject-matter experts from industrial organizations results in the irreversible loss of tacit knowledge that is rarely captured through conventional documentation practices. This paper proposes Expert Mind, an experimental system that leverages Retrieval-Augmented Generation (RAG), large language models (LLMs), and multimodal capture techniques to preserve, structure, and make queryable the deep expertise of organizational knowledge holders. 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.

翻译：领域专家离开工业组织会导致隐性知识的不可逆损失，这类知识很少能通过传统文档实践得以留存。本文提出 Expert Mind，一个实验性系统，它利用检索增强生成（RAG）、大语言模型（LLM）和多模态捕获技术，以保存、结构化组织知识持有者的深层专业知识，并使其可查询。本研究基于能源领域的具体背景——该领域数十年的运营经验正面临因劳动力老龄化而流失的风险——描述了系统架构、处理流程、伦理框架和评估方法。所提出的系统通过结构化访谈、有声思维会话和文本语料库摄入来解决知识获取问题，这些内容随后被嵌入向量存储库，并可通过对话界面进行查询。初步设计考量表明，Expert Mind 能显著减少知识传递延迟并提高入职培训效率。知情同意、知识产权和删除权等伦理维度被作为首要设计约束加以处理。