Chunking

Chunking is the process of breaking large documents into smaller pieces (chunks) that can be individually embedded, indexed, and retrieved in a RAG or search system. Since language models have limited context windows and embedding models work best with shorter texts, documents must be divided into appropriately sized segments. There are several chunking strategies. Fixed-size chunking splits text at regular intervals (e.g., every 500 tokens) with optional overlap. Recursive chunking splits on natural boundaries like paragraphs, sentences, then characters. Semantic chunking uses embedding similarity to identify natural topic boundaries. Document-structure-aware chunking respects headings, sections, and logical divisions. The choice of chunk size and strategy involves important trade-offs. Smaller chunks enable more precise retrieval but may lose context. Larger chunks preserve more context but may include irrelevant information and reduce retrieval precision. Overlapping chunks can help ensure that no information is lost at chunk boundaries.

Chunking is one of the most impactful — yet often overlooked — components of a RAG system. Poor chunking can cause the system to retrieve irrelevant content, miss important information that spans chunk boundaries, or return chunks that are too short to be useful. For business applications processing diverse document types (contracts, reports, policies, manuals), a one-size-fits-all chunking approach rarely works well. Documents with tables need table-aware chunking. Legal documents need section-aware chunking. Technical manuals need heading-aware chunking. Investing in document-type-specific chunking strategies pays significant dividends in system quality. Practical recommendations include starting with recursive chunking as a baseline, testing different chunk sizes (typically 200-1000 tokens), including chunk overlap (10-20% of chunk size), and preserving metadata (source document, section heading, page number) with each chunk for context and attribution.