mlsanityData to 14 Oct 2026

Enhancing Retrieval-Augmented Generation: A Study of Best Practices

arXiv 2501.07391 ↗First posted 13 Jan 2025cs.CL cs.AIcited by 23

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

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) systems have recently shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses. However, the influence of various components and configurations within RAG systems remains underexplored. A comprehensive understanding of these elements is essential for tailoring RAG systems to complex retrieval tasks and ensuring optimal performance across diverse applications. In this paper, we develop several advanced RAG system designs that incorporate query expansion, various novel retrieval strategies, and a novel Contrastive In-Context Learning RAG. Our study systematically investigates key factors, including language model size, prompt design, document chunk size, knowledge base size, retrieval stride, query expansion techniques, Contrastive In-Context Learning knowledge bases, multilingual knowledge bases, and Focus Mode retrieving relevant context at sentence-level. Through extensive experimentation, we provide a detailed analysis of how these factors influence response quality. Our findings offer actionable insights for developing RAG systems, striking a balance between contextual richness and retrieval-generation efficiency, thereby paving the way for more adaptable and high-performing RAG frameworks in diverse real-world scenarios. Our code and implementation details are publicly available.

Key facts. Cited by 23 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 25 of its 39 references that are in the map. First posted on arXiv 13 Jan 2025.

Builds on

25 of 39 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  6. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  7. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
  8. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  9. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  10. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  11. 2021MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontierscited by 470
  12. 2024Continual Learning of Large Language Models: A Comprehensive Surveycited by 234
12 more
  1. 2024ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidencecited by 113
  2. 2024Tug-of-War Between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Modelscited by 93
  3. 2024Searching for Best Practices in Retrieval-Augmented Generationcited by 83
  4. 2024Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Basescited by 77
  5. 2023WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipediacited by 67
  6. 2024Can Perplexity Reflect Large Language Model's Ability in Long Text Understanding?cited by 45
  7. 2022NEWTS: A Corpus for News Topic-Focused Summarizationcited by 27
  8. 2024RAGGED: Towards Informed Design of Scalable and Stable RAG Systemscited by 24
  9. 2024LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understandingcited by 24
  10. 2024Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topicscited by 9
  11. 2023Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectivescited by 5
  12. 2024Enhancing Knowledge Retrieval with Topic Modeling for Knowledge-Grounded Dialoguecited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 13; 2026: 10.

23 papers.

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  1. 2025Get Experience from Practice: LLM Agents with Record & Replaycited by 23
  2. 2025FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generationcited by 17
  3. 2025Randomly Removing 50% of Dimensions in Text Embeddings has Minimal Impact on Retrieval and Classification Taskscited by 8
  4. 2025Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domaincited by 6
  5. 2025Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysiscited by 5
  6. 2025Less LLM, More Documents: Searching for Improved RAGcited by 4
  7. 2025Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluationcited by 3
  8. 2025LLM-based Question-Answer Framework for Sensor-driven HVAC System Interactioncited by 2
  9. 2025A Large-Language-Model Framework for Automated Humanitarian Situation Reportingcited by 2
  10. 2026From BM25 to Corrective RAG: Benchmarking Retrieval Strategies for Text-and-Table Documentscited by 2
  11. 2026Conversational Query Engine for Mixed-Modality Heterogeneous Enterprise Data Sourcescited by 2
  12. 2025A Decentralized Retrieval Augmented Generation System with Source Reliabilities Secured on Blockchaincited by 1
11 more
  1. 2026AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflowscited by 1
  2. 2026Evaluating Social Bias in RAG Systems: When External Context Helps and Reasoning Hurtscited by 1
  3. 2026VDAR-Router: Adaptive LLMs Routing via Verbalized Query Difficulty Analysis Retrievalcited by 1
  4. 2025A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generationcited by 0
  5. 2025A Lightweight Multi-Expert Generative Language Model System for Engineering Information and Knowledge Extractioncited by 0
  6. 2025Advancing Shared and Multi-Agent Autonomy in Underwater Missions: Integrating Knowledge Graphs and Retrieval-Augmented Generationcited by 0
  7. 2026AI Engineering Blueprint for On-Premises Retrieval-Augmented Generation Systemscited by 0
  8. 2026Multimodal QUD: Inquisitive Questions from Scientific Figurescited by 0
  9. 2026Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answeringcited by 0
  10. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0
  11. 2026Configuring RAG Systems: What Breaks, Where Originates, and How to Fixcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  2. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  3. 2024Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems
  4. 2025Dynamic Context Selection for Retrieval-Augmented Generation: Mitigating Distractors and Positional Bias
  5. 2024Introducing a new hyper-parameter for RAG: Context Window Utilization
  6. 2025RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation
  7. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  8. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey