Chunking creates smaller retrieval units from long documents. Whole-document embeddings can be too broad; tiny chunks can lose definitions, exceptions, and surrounding context. The ideal chunk contains a coherent idea and enough context to interpret it independently.

Chunking depends on source structure, query type, embedding model, and context budget. Token-based sizes align better with model limits than character counts, but semantic boundaries are often more important than a universal threshold. Evaluate rather than copying defaults.