Question

A financial services company built a Bedrock Knowledge Base over regulatory filings. Analysts complain that queries containing exact regulatory codes (e.g., 'Rule 15c3-3') and specific ticker symbols often return semantically related but incorrect documents, while conceptual questions like 'what are liquidity requirements' work well. The team wants to improve retrieval accuracy for both keyword-exact and conceptual queries without building a separate search system. What should they configure?

Accepted Answer

Change the search type on the Knowledge Base retrieval configuration to HYBRID so both semantic (dense vector) and keyword (sparse) matching are combined

Answer

Switch the embedding model to one with a higher dimension count to capture the exact tokens in regulatory codes

Answer

Reduce the chunk size so each regulatory code appears in its own isolated chunk for more precise vector matches

Answer

Increase the numberOfResults parameter and rely on the foundation model to filter out the incorrect documents during generation