An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs
This addresses the problem of RAG deployment for industrial SMEs, but it is incremental as it applies an existing method engineering approach to a specific context.
The paper tackled the challenge of deploying Retrieval-Augmented Generation (RAG) tools in Small and Medium Enterprises (SMEs) with limited resources, introducing EASI-RAG, an agile method that enabled implementation in under a month, resulting in fast deployment, high user adoption, and accurate answers.
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emerged as a powerful solution to mitigate the limitations of Large Language Models (LLMs), such as hallucinations and outdated knowledge.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.