Chunking Methods on Retrieval-Augmented Generation - Effectiveness Evaluation Against Computational Cost and Limitations

2026-05-30

AI Summary

This study addresses the lack of systematic evaluation of text chunking strategies in existing Retrieval-Augmented Generation (RAG) systems across diverse scenarios, which hinders objective assessment of their performance and applicability. For the first time, it conducts controlled, cross-task, and cross-data-type experiments within a unified framework to comparatively analyze mainstream chunking methods—including fixed-length, semantic, and emerging techniques. The findings reveal that most advanced chunking approaches exhibit limited generalizability, performing effectively only in specific contexts. Furthermore, the work quantifies the trade-offs between effectiveness and computational cost across different strategies and highlights critical yet often overlooked limitations of chunking as a preprocessing step. These insights provide empirical grounding for the design and optimization of RAG systems.

Abstract

Retrieval-Augmented Generation (RAG) has demonstrated significant capabilities in enhancing the performance of Large Language Models (LLMs). One of the key tasks in RAG systems is the chunking process. Traditionally, fixed-size chunking and semantic chunking have been the standard approaches. However, interest in chunking strategies has been increasing, leading to a growing number of proposed methods that often claim improved performance over these conventional techniques. Many of these approaches are tailored to specific use cases and data types, with limited evidence of their effectiveness across diverse scenarios. As a result, it remains challenging to directly compare different techniques and assess their relative strengths. To the best of our knowledge, this study is the first to systematically evaluate the effectiveness of a wide range of chunking methods and emphasize the underlying challenges of chunking strategies in RAG systems. While chunking is commonly treated as a simple preprocessing step, we show that it introduces a range of impactful and often overlooked issues.

Problem

• chunking
• Retrieval-Augmented Generation
• Large Language Models
• text segmentation
• evaluation

Innovation

• chunking methods
• Retrieval-Augmented Generation
• systematic evaluation
• computational cost
• semantic chunking