Recursive Character Text Splitter consultants

The Recursive Character Text Splitter node in n8n breaks long text documents into smaller chunks by recursively splitting on natural text boundaries — paragraphs first, then sentences, then words. This hierarchical approach produces cleaner chunks than fixed-length splitting because it respects the structure of the original text. The result is chunks that are more coherent and more useful for downstream AI processing.

When preparing documents for embedding generation, summarisation, or classification, chunk quality directly impacts the quality of your results. Splitting in the middle of a sentence produces fragments that lose meaning in isolation. The recursive approach avoids this by trying the largest separators first (double newlines for paragraphs) and only falling back to smaller separators (single newlines, spaces) when a chunk still exceeds the target size. This gives you the best balance between chunk size consistency and content coherence.

This node is a fundamental building block in retrieval-augmented generation (RAG) pipelines. After splitting, each chunk is typically passed through an embedding model and stored in a vector database for semantic search. The quality of your splits directly affects retrieval precision — well-formed chunks that represent complete thoughts or sections produce more relevant search results. Our patient data entry automation project used careful text processing to extract and classify medical information accurately.

If you are building document processing workflows and want guidance on chunking strategies for your specific data, our AI consulting team and data processing specialists can help you design an approach that maximises the quality of your AI outputs.