An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs
Mathieu Bourdin, Anas Neumann, Thomas Paviot, Robert Pellerin, Samir Lamouri

TL;DR
This paper presents EASI-RAG, an agile method to deploy Retrieval-Augmented Generation tools in industrial SMEs, enabling quick, accurate, and reliable NLP solutions despite limited resources.
Contribution
The paper introduces EASI-RAG, a structured, agile method based on method engineering principles for deploying RAG systems in industrial SME environments.
Findings
Rapid deployment within a month by non-experts
High user adoption and accurate responses
Improved data reliability and system trustworthiness
Abstract
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emerged as a powerful solution to mitigate the limitations of Large Language Models (LLMs), such as hallucinations and outdated knowledge.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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsService-Oriented Architecture and Web Services · Educational Technology and Assessment · Collaboration in agile enterprises
