An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs Article Swipe

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Mathieu Bourdin , Anas Neumann , Thomas Paviot , Robert Pellerin , Samir Lamouri ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2508.21024

Retrieval-Augmented Generation (RAG) has emerged as a powerful solution to mitigate the limitations of Large Language Models (LLMs), such as hallucinations and outdated knowledge. However, deploying RAG-based tools in Small and Medium Enterprises (SMEs) remains a challenge due to their limited resources and lack of expertise in natural language processing (NLP). This paper introduces EASI-RAG, Enterprise Application Support for Industrial RAG, a structured, agile method designed to facilitate the deployment of RAG systems in industrial SME contexts. EASI-RAG is based on method engineering principles and comprises well-defined roles, activities, and techniques. The method was validated through a real-world case study in an environmental testing laboratory, where a RAG tool was implemented to answer operators queries using data extracted from operational procedures. The system was deployed in under a month by a team with no prior RAG experience and was later iteratively improved based on user feedback. Results demonstrate that EASI-RAG supports fast implementation, high user adoption, delivers accurate answers, and enhances the reliability of underlying data. This work highlights the potential of RAG deployment in industrial SMEs. Future works include the need for generalization across diverse use cases and further integration with fine-tuned models.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2508.21024
PDF: https://arxiv.org/pdf/2508.21024
OA Status: green
OpenAlex ID: https://openalex.org/W4414451928

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DOI: https://doi.org/10.48550/arxiv.2508.21024

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Title: An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-08-28

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Authors: Mathieu Bourdin, Anas Neumann, Thomas Paviot, Robert Pellerin, Samir Lamouri

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Landing page: https://arxiv.org/abs/2508.21024

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Cited by: 0

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