RAG Chunking Strategies: The 2026 Benchmark Guide

The text discusses various chunking strategies in Retrieval-Augmented Generation (RAG) pipelines, emphasizing the importance of choosing the right method to optimize retrieval quality. Recursive character splitting with 512 tokens and 50 to 100 tokens of overlap is recommended as a default approach due to its high accuracy and efficiency in benchmarks, outperforming more costly alternatives. The text highlights that chunking strategy can influence retrieval quality as much as the choice of embedding model, with studies showing fixed-size chunking often surpassing semantic chunking on realistic datasets. Additionally, the document outlines when different chunking strategies should be applied, noting that smaller chunks may lose context, while larger ones can dilute relevance. It also addresses the implications of chunking strategies on model selection and retrieval outcomes, suggesting that chunking should be tailored to document type, with testing and adjustments made based on specific use cases.

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