Expert Mind: A Retrieval-Augmented Architecture for Expert Knowledge Preservation in the Energy Sector
On the map: Retrieval-augmented generation → RAG pipeline design and evaluation
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 5 of its 16 references that are in the map. First posted on arXiv 15 Mar 2026.
Builds on
5 of 16 references.
References · 5
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
- 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 126
- 2022Retrieval-Augmented Multimodal Language Modelingcited by 125
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