Recursive Chunking

Recursive chunking is a hierarchical splitting strategy that attempts to split text at meaningful natural boundaries first, falling back to coarser boundaries only when chunks exceed the target size. The LangChain RecursiveCharacterTextSplitter, which popularized the approach, tries to split first on double newlines (paragraph breaks), then single newlines (line breaks), then sentences, then spaces, and finally characters — only descending the hierarchy when higher-level splits produce chunks larger than the target. This preserves natural semantic units when possible while guaranteeing that no chunk exceeds the maximum size. Recursive chunking is the recommended default in most RAG frameworks because it balances simplicity, performance, and quality without requiring domain-specific tuning. Variants include language-specific separator lists, markdown-aware separators that respect heading levels, and code-aware separators that respect function and class boundaries. AI governance teams adopting recursive chunking document the separator list and target size as part of their embedding pipeline configuration. The technique works well across most content types but is sometimes outperformed by purpose-built parsers for highly structured content like tables, code, or legal documents.

Recursive chunking in Centralpoint: Centralpoint supports recursive chunking strategies through its RAG pipeline integration, with per-skill configuration of separators and target chunk size. The model-agnostic platform routes generation to any LLM, meters tokens, keeps prompts local, and deploys recursively-chunked chatbots through one line of JavaScript.


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