Data Quality Challenges in Retrieval-Augmented Generation
On the map: Retrieval-augmented generation → RAG pipeline design and evaluation
Abstract (arXiv)
Organizations increasingly adopt Retrieval-Augmented Generation (RAG) to enhance Large Language Models with enterprise-specific knowledge. However, current data quality (DQ) frameworks have been primarily developed for static datasets, and only inadequately address the dynamic, multi-stage nature of RAG systems. This study aims to develop DQ dimensions for this new type of AI-based systems. We conduct 16 semi-structured interviews with practitioners of leading IT service companies. Through a qualitative content analysis, we inductively derive 15 distinct DQ dimensions across the four processing stages of RAG systems: data extraction, data transformation, prompt & search, and generation. Our findings reveal that (1) new dimensions have to be added to traditional DQ frameworks to also cover RAG contexts; (2) these new dimensions are concentrated in early RAG steps, suggesting the need for front-loaded quality management strategies, and (3) DQ issues transform and propagate through the RAG pipeline, necessitating a dynamic, step-aware approach to quality management.
Key facts. Cited by 1 paper 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 15 of its 46 references that are in the map. First posted on arXiv 1 Oct 2025.
Builds on
15 of 46 references.
Closest ancestors · 5
Same direction, newest first
- 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely23 Sep
- 2024RAG Does Not Work for Enterprises31 May
- 2024Seven Failure Points When Engineering a Retrieval Augmented Generation System11 Jan
- 2023Ragas: Automated Evaluation of Retrieval Augmented Generation26 Sep
- 2023A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture3 Sep
Other references · 10
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
- 2023Extending Context Window of Large Language Models via Positional Interpolationcited by 721
- 2024Trustworthiness in Retrieval-Augmented Generation Systems: A Surveycited by 83
- 2024The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunitiescited by 63
- 2023Data-centric AI: Perspectives and Challengescited by 43
- 2022Data-Centric Artificial Intelligencecited by 31
- 2024Speech Retrieval-Augmented Generation without Automatic Speech Recognitioncited by 14
- 2020Artificial Intelligence Governance for Businessescited by 8
- 2025A Survey of LLM Alignment: Instruction Understanding, Intention Reasoning, and Reliable Generationcited by 3
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Citing papers per year: 2026: 1.
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Close in wording, no citation either way
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- 2024RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation
- 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey
- 2024Retrieval Augmented Generation Systems: Automatic Dataset Creation, Evaluation and Boolean Agent Setup
- 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
- 2025An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs
- 2024Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems