Best Chunking Strategies for RAG (and LLMs) in 2026

In a detailed exploration of seven document chunking strategies, the text emphasizes the importance of choosing the right method for optimizing retrieval-augmented generation (RAG) systems. Recursive character splitting is highlighted as a generally effective starting point for most text content, balancing simplicity with context preservation. Page-level chunking shows the best performance for paginated documents, as evidenced by NVIDIA's benchmarks, while semantic chunking offers improved recall, albeit at a higher computational cost. The text underscores how each strategy, ranging from size-based to LLM-based chunking, has distinct trade-offs in terms of context preservation, computational costs, and retrieval precision. The document also outlines the role of chunking in a complete RAG pipeline, emphasizing the need for clean data input to enhance the effectiveness of any chunking strategy. Additionally, the significance of chunk size, overlap, and strategy selection based on document types and query patterns is discussed, with practical examples using various tools like LangChain and Firecrawl to illustrate the implementation of these strategies.

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