mlsanityData to 7 Oct 2026

Introducing a new hyper-parameter for RAG: Context Window Utilization

arXiv 2407.19794 ↗First posted 29 Jul 2024cs.CL cs.ETcited by 8

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

Abstract (arXiv)

This paper introduces a new hyper-parameter for Retrieval-Augmented Generation (RAG) systems called Context Window Utilization. RAG systems enhance generative models by incorporating relevant information retrieved from external knowledge bases, improving the factual accuracy and contextual relevance of generated responses. The size of the text chunks retrieved and processed is a critical factor influencing RAG performance. This study aims to identify the optimal chunk size that maximizes answer generation quality. Through systematic experimentation, we analyze the effects of varying chunk sizes on the efficiency and effectiveness of RAG frameworks. Our findings reveal that an optimal chunk size balances the trade-off between providing sufficient context and minimizing irrelevant information. These insights are crucial for enhancing the design and implementation of RAG systems, underscoring the importance of selecting an appropriate chunk size to achieve superior performance.

Key facts. Cited by 8 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 16 of its 35 references that are in the map. First posted on arXiv 29 Jul 2024.

Builds on

16 of 35 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems1 Jul
  2. 2024Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalability17 Jun
  3. 2024COS-Mix: Cosine Similarity and Distance Fusion for Improved Information Retrieval2 Jun
  4. 2024Seven Failure Points When Engineering a Retrieval Augmented Generation System11 Jan

Other references · 12

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  3. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  4. 2024RULER: What's the Real Context Size of Your Long-Context Language Models?cited by 1,270
  5. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,050
  6. 2024Long-context LLMs Struggle with Long In-context Learningcited by 285
  7. 2024BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystackcited by 211
  8. 2021Semantic Answer Similarity for Evaluating Question Answering Modelscited by 47
  9. 2024LumberChunker: Long-Form Narrative Document Segmentationcited by 36
  10. 2022Exploring Dual Encoder Architectures for Question Answeringcited by 19
  11. 2023Keyword Augmented Retrieval: Novel framework for Information Retrieval integrated with speech interfacecited by 11
  12. 2024Benchmarking LLMs for Environmental Review and Permittingcited by 3

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Cited by year

Citing papers per year: 2024: 3; 2025: 5.

8 papers.

  1. 2024A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directionscited by 25
  2. 2025Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoningcited by 17
  3. 2025KnowsLM: A framework for evaluation of small language models for knowledge augmentation and humanised conversationscited by 6
  4. 2024RAG Playground: A Framework for Systematic Evaluation of Retrieval Strategies and Prompt Engineering in RAG Systemscited by 4
  5. 2025UniC-RAG: Universal Knowledge Corruption Attacks to Retrieval-Augmented Generationcited by 3
  6. 2025Prescriptive Agents based on RAG for Automated Maintenance (PARAM)cited by 2
  7. 2025i-LAVA: Insights on Low Latency Voice-2-Voice Architecture for Agentscited by 2
  8. 2024CultureVo: The Serious Game of Utilizing Gen AI for Enhancing Cultural Intelligencecited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices
  2. 2025Dynamic Context Selection for Retrieval-Augmented Generation: Mitigating Distractors and Positional Bias
  3. 2024In Defense of RAG in the Era of Long-Context Language Models
  4. 2025Optimizing Retrieval-Augmented Generation: Analysis of Hyperparameter Impact on Performance and Efficiency
  5. 2024Searching for Best Practices in Retrieval-Augmented Generation
  6. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  7. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  8. 2025RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation