mlsanityData to 7 Oct 2026

RAG Does Not Work for Enterprises

arXiv 2406.04369 ↗First posted 31 May 2024cs.SE cs.AIcited by 15

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

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) improves the accuracy and relevance of large language model outputs by incorporating knowledge retrieval. However, implementing RAG in enterprises poses challenges around data security, accuracy, scalability, and integration. This paper explores the unique requirements for enterprise RAG, surveys current approaches and limitations, and discusses potential advances in semantic search, hybrid queries, and optimized retrieval. It proposes an evaluation framework to validate enterprise RAG solutions, including quantitative testing, qualitative analysis, ablation studies, and industry case studies. This framework aims to help demonstrate the ability of purpose-built RAG architectures to deliver accuracy and relevance improvements with enterprise-grade security, compliance and integration. The paper concludes with implications for enterprise deployments, limitations, and future research directions. Close collaboration between researchers and industry partners may accelerate progress in developing and deploying retrieval-augmented generation technology.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 0 of its 1 references that are in the map. First posted on arXiv 31 May 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 11; 2026: 3.

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  1. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 85
  2. 2024RAGEval: Scenario Specific RAG Evaluation Dataset Generation Frameworkcited by 39
  3. 2025Can LLMs Help You at Work? A Sandbox for Evaluating LLM Agents in Enterprise Environmentscited by 15
  4. 2025The RAG Paradox: A Black-Box Attack Exploiting Unintentional Vulnerabilities in Retrieval-Augmented Generation Systemscited by 14
  5. 2025Towards Practical GraphRAG: Efficient Knowledge Graph Construction and Hybrid Retrieval at Scalecited by 9
  6. 2025Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understandingcited by 9
  7. 2025RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practicecited by 6
  8. 2025Position: Foundation Models for Tabular Data within Systemic Contexts Need Groundingcited by 5
  9. 2025FinBloom: Knowledge Grounding Large Language Model with Real-time Financial Datacited by 1
  10. 2025Pseudo-Knowledge Graph: Meta-Path Guided Retrieval and In-Graph Text for RAG-Equipped LLMcited by 1
  11. 2025Data Quality Challenges in Retrieval-Augmented Generationcited by 1
  12. 2025AI-Driven Climate Policy Scenario Generation for Sub-Saharan Africacited by 0
3 more
  1. 2026Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive Integrationcited by 0
  2. 2026AI Engineering Blueprint for On-Premises Retrieval-Augmented Generation Systemscited by 0
  3. 2026W-RAG: Source-Aware Retrieval for Enterprise Document Generation from Heterogeneous Knowledge Basescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspective
  2. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  3. 2025Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
  4. 2025Secure Multifaceted-RAG for Enterprise: Hybrid Knowledge Retrieval with Security Filtering
  5. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  6. 2024Seven Failure Points When Engineering a Retrieval Augmented Generation System
  7. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  8. 2025Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation