Question

A financial services company is building a RAG assistant using Amazon Bedrock Knowledge Bases over lengthy regulatory filings. During testing, they find that small chunks retrieve precisely relevant passages but the foundation model lacks enough surrounding context to generate coherent, complete answers. Larger chunks improve answer coherence but degrade retrieval precision because embeddings become diluted across many topics. They want to preserve precise retrieval while giving the model broader context at generation time. Which Bedrock Knowledge Bases chunking approach best addresses this?

Accepted Answer

Use hierarchical (parent-child) chunking so small child chunks are embedded and searched, while the larger parent chunk is returned to the model for generation.

Answer

Switch to fixed-size chunking with a very large token size and rely on metadata filtering to narrow results.

Answer

Disable chunking entirely (no chunking) so each document is embedded as a single vector for maximum context.

Answer

Use semantic chunking with a low breakpoint threshold to force many small chunks and increase the number of retrieved results.