Chunking for RAG: best practices

Chunking is a critical preprocessing step in Retrieval-Augmented Generation (RAG) systems, aimed at improving retrieval precision by dividing documents into manageable pieces that fit within the context window limits of language models and embedding models. Large chunks can impede precision due to their coarse representations and potential mixing of unrelated topics, while smaller chunks allow for more precise matching and retrieval of relevant information. Traditional methods like character or sentence-level chunking often disrupt the document structure, whereas smart chunking strategies, such as those offered by Unstructured, preserve the semantic integrity of documents. These strategies utilize document partitioning to maintain logical units like paragraphs, sections, and tables, ensuring that chunks are semantically meaningful and contextually appropriate. Unstructured's smart chunking offers four strategies: basic, by title, by page, and by similarity, which enhance retrieval precision by respecting document structure or topical similarity. This approach is adaptable across various document types, facilitating experimentation with chunk sizes and strategies to optimize RAG performance.

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