RAG Chunking Strategies: The 2026 Benchmark Guide

In the realm of retrieval-augmented generation (RAG) applications, chunking strategies significantly impact retrieval and answer accuracy, often more so than the choice of embedding model. Recursive character splitting at 512 tokens with 50 to 100 tokens of overlap is the widely recommended default, validated by benchmarks for its high accuracy and efficiency, requiring no model calls. Different chunking strategies, such as fixed-size, semantic, and page-level chunking, are suited to specific document types and contexts, with each having distinct strengths and weaknesses. While recursive splitting generally offers the best starting point for most RAG systems, semantic chunking can excel in retrieval recall but may falter in end-to-end accuracy due to small fragment sizes. The efficacy of a chunking strategy is context-dependent, influenced by factors like document structure and query type, and should be tailored to the specific corpus and retrieval needs. Testing and optimizing chunking configurations on real-world datasets is essential for maximizing retrieval performance, and while semantic chunking might appear superior in theory, practical gains often do not justify its computational cost.