What is Chunking?
Chunking is the process of dividing large documents or text passages into smaller, more manageable segments for storage, embedding, and retrieval in AI systems. This segmentation is necessary because embedding models have input length limitations, and smaller chunks often provide more focused and relevant retrieval results than entire documents. Effective chunking balances the need for self-contained, meaningful segments with the practical constraints of model context windows.
Different chunking strategies exist to handle various types of content. Simple approaches split text by character count or token count, while more sophisticated methods respect document structure by splitting on sentence boundaries, paragraphs, or semantic breaks. Advanced techniques may use sliding windows with overlap between chunks to prevent important information from being split awkwardly, or employ recursive splitting that adapts the chunk size based on document structure.
The choice of chunking strategy significantly impacts the performance of RAG systems and agent memory. Chunks that are too small may lack sufficient context, while chunks that are too large may contain irrelevant information that dilutes relevance scores. The optimal chunk size and strategy depend on factors like the type of content, the embedding model's capabilities, the expected query patterns, and the downstream application's requirements for context and precision.