Chunking

Chunking breaks source documents into smaller text segments before they are converted into embeddings and stored in a vector database. In a Retrieval-Augmented Generation (RAG) pipeline built with Amazon Bedrock Knowledge Bases, chunking happens at ingestion time and directly shapes retrieval quality at inference time. Note the distinction from embedding: chunking sets the unit of text that gets retrieved, while embedding determines how that unit is represented numerically. Chunk size is a balancing act — larger chunks preserve more surrounding context but can flood the prompt with irrelevant content, whereas smaller chunks sharpen precision but may omit the context needed for a coherent answer.