Recursive Character Text Splitter
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.
About this tool
Overview
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.
Features
- Hierarchical Splitting: Uses ["\n\n", "\n", " ", ""] separators in order
- Natural Boundaries: Breaks at paragraphs, sentences, and words
- Configurable Chunk Size: Typically 400-512 tokens
- Overlap Support: 10-20% overlap to maintain context
- Language Aware: Can adapt to different document structures
- Deterministic: Consistent results for same input
Best Practices (2026)
- Recommended Starting Point: 400-512 tokens with 10-20% overlap
- Industry Standard: Most practical default strategy
- Benchmark Performance: 69-89.5% accuracy in recent benchmarks
- Optimal Configuration: 400 tokens achieves 88.1-89.5% accuracy
Performance
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.
Use Cases
- Default chunking for RAG applications
- Document processing pipelines
- Knowledge base creation
- Long-form content indexing
- General-purpose text splitting
Comparison
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.
Integration
Available in LangChain, LlamaIndex, and other RAG frameworks. Default choice in most implementations.
Pricing
Free - it's an algorithmic approach, not a paid service.
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