Document chunking strategy that splits text at hierarchical boundaries like paragraphs, sentences, or headings. Industry-standard approach recommended as starting point with 400-512 tokens and 10-20% overlap for optimal RAG performance.
Recursive Character Text Splitter is the most widely recommended chunking strategy for RAG applications in 2026. It uses hierarchical separators to break text at natural boundaries, maintaining semantic coherence.
Vecta's February 2026 benchmark of 7 strategies across 50 academic papers placed recursive 512-token splitting first at 69% accuracy. RecursiveCharacterTextSplitter achieved 85.4-89.5% recall, with best performance at 400 tokens.
Outperforms semantic chunking in practical benchmarks while being more cost-effective and predictable. Move to semantic or page-level chunking only if metrics show you need extra performance.
Available in LangChain, LlamaIndex, and other RAG frameworks. Default choice in most implementations.
Free - it's an algorithmic approach, not a paid service.
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