Retaining explicit and implicit knowledge with RAG-enhanced Generative AI
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Author (Corporation)
Publication date
2026
Type of student thesis
Course of study
Collections
Type
04B - Conference paper
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Editor (Corporation)
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Parent work
2026 IEEE Conference on Artificial Intelligence (CAI)
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DOI of the original publication
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Related research data
Series
Series number
Volume
Issue / Number
Pages / Duration
403-406
Patent number
Publisher / Publishing institution
IEEE
Place of publication / Event location
Granada
Edition
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Programming language
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Practice partner / Client
Abstract
Organizational knowledge, both explicit in documents and implicit in employees’ minds, is a key source of competitive advantage but is often lost through turnover and inadequate knowledge management. This study demonstrates how Generative AI (GenAI) combined with Retrieval Augmented Generation (RAG) can help retain and reuse such knowledge. Using the Design Science Research methodology, a GenAI system was developed and applied in a case study of the purchasing department of a major European engineering and technology company. The solution uses transcripts from expert debriefing interviews to elicit and categorize implicit knowledge at both surface and deep levels. The AI system interprets and contextualizes expert insights, transforming them into accessible organizational knowledge. The resulting artefact enables efficient retrieval and reuse of codified expertise and is transferable across business contexts. Workshop evaluations confirmed its effectiveness in capturing and applying implicit knowledge, demonstrating that GenAI with RAG offers a practical approach to mitigating knowledge loss and leveraging organizational expertise more effectively.
Keywords
Event
2026 IEEE Conference on Artificial Intelligence (CAI)
Exhibition start date
Exhibition end date
Conference start date
08.05.2026
Conference end date
10.05.2026
Date of the last check
ISBN
979-8-3315-6039-3
979-8-3315-6040-9
979-8-3315-6040-9
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
peer-reviewed
Open access category
Closed
Citation
Miliaev, S., Hinkelmann, K., & Eisenbart, B. (2026). Retaining explicit and implicit knowledge with RAG-enhanced Generative AI. 2026 IEEE Conference on Artificial Intelligence (CAI), 403–406. https://doi.org/10.1109/cai68641.2026.11536520