mlsanityData to 14 Oct 2026

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

arXiv 2508.21024 ↗First posted 28 Aug 2025cs.CL cs.IRcited by 2

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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emerged as a powerful solution to mitigate the limitations of Large Language Models (LLMs), such as hallucinations and outdated knowledge.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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 40 of its 68 references that are in the map. First posted on arXiv 28 Aug 2025.

Builds on

40 of 68 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025GINGER: Grounded Information Nugget-Based Generation of Responses23 Mar
  2. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation11 Mar
  3. 2025RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practice20 Feb
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec
  5. 2023Ragas: Automated Evaluation of Retrieval Augmented Generation26 Sep

Other references · 35

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  5. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  6. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  7. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,407
  8. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  9. 2022MTEB: Massive Text Embedding Benchmarkcited by 640
  10. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 619
  11. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 560
  12. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
23 more
  1. 2024VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documentscited by 245
  2. 2023Query Rewriting for Retrieval-Augmented Large Language Modelscited by 213
  3. 2024Is Temperature the Creativity Parameter of Large Language Models?cited by 93
  4. 2024Searching for Best Practices in Retrieval-Augmented Generationcited by 83
  5. 2025Retrieval-Augmented Generation with Hierarchical Knowledgecited by 47
  6. 2024Financial Report Chunking for Effective Retrieval Augmented Generationcited by 46
  7. 2024Is Semantic Chunking Worth the Computational Cost?cited by 43
  8. 2024DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMscited by 24
  9. 2025TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoningcited by 24
  10. 2025A Survey on Privacy Risks and Protection in Large Language Modelscited by 21
  11. 2020Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracycited by 20
  12. 2025MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation Systemcited by 19
  13. 2025Rethinking Chunk Size For Long-Document Retrieval: A Multi-Dataset Analysiscited by 17
  14. 2024Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation Systemscited by 12
  15. 2024Golden-Retriever: High-Fidelity Agentic Retrieval Augmented Generation for Industrial Knowledge Basecited by 11
  16. 2025Emulating Retrieval Augmented Generation via Prompt Engineering for Enhanced Long Context Comprehension in LLMscited by 9
  17. 2025S2 Chunking: A Hybrid Framework for Document Segmentation Through Integrated Spatial and Semantic Analysiscited by 8
  18. 2025A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunkingcited by 7
  19. 2025CHORUS: Zero-shot Hierarchical Retrieval and Orchestration for Generating Linear Programming Codecited by 3
  20. 2025A RAG-Based Institutional Assistantcited by 2
  21. 2023Unveiling the Life Cycle of User Feedback: Best Practices from Software Practitionerscited by 1
  22. 2024Developing a Llama-Based Chatbot for CI/CD Question Answering: A Case Study at Ericssoncited by 1
  23. 2024LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Modelscited by 1

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understandingcited by 9
  2. 2025SPAR: Session-based Pipeline for Adaptive Retrieval on Legacy File Systemscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
  2. 2024RAG Does Not Work for Enterprises
  3. 2024Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspective
  4. 2024Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalability
  5. 2024Deploying Large Language Models With Retrieval Augmented Generation
  6. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  7. 2025Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation
  8. 2024Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems