Chunk size is a critical hyperparameter in RAG pipelines. Small chunks (100–200 tokens) improve retrieval precision by matching specific passages but may lose surrounding context needed for understanding. Large chunks (500–1000+ tokens) preserve context but can dilute the embedding's focus and consume more of the LLM's context window. Optimal chunk size depends on document structure, embedding model capabilities, and the downstream task.