AIP-C01 · Question #54
A pharmaceutical company is developing a Retrieval Augmented Generation (RAG) application that uses an Amazon Bedrock knowledge base. The knowledge base uses Amazon OpenSearch Service as a data…
The correct answer is B. Configure the knowledge base to use hierarchical chunking. Use parent chunks that contain. Option B is the best solution because hierarchical chunking is specifically designed to preserve broader semantic context while still enabling precise retrieval at paragraph or sub-paragraph granularity. The problem described--answers citing irrelevant sections when a query…
Question
A pharmaceutical company is developing a Retrieval Augmented Generation (RAG) application that uses an Amazon Bedrock knowledge base. The knowledge base uses Amazon OpenSearch Service as a data source for more than 25 million scientific papers. Users report that the application produces inconsistent answers that cite irrelevant sections of papers when queries span methodology, results, and discussion sections of the papers. The company needs to improve the knowledge base to preserve semantic context across related paragraphs on the scale of the entire corpus of data. Which solution will meet these requirements?
Options
- AConfigure the knowledge base to use fixed-size chunking. Set a 300-token maximum chunk size
- BConfigure the knowledge base to use hierarchical chunking. Use parent chunks that contain
- CConfigure the knowledge base to use semantic chunking. Use a buffer size of 1 and a breakpoint
- DConfigure the knowledge base not to use chunking. Manually split each document into separate
How the community answered
(22 responses)- A5% (1)
- B77% (17)
- C5% (1)
- D14% (3)
Explanation
Option B is the best solution because hierarchical chunking is specifically designed to preserve broader semantic context while still enabling precise retrieval at paragraph or sub-paragraph granularity. The problem described--answers citing irrelevant sections when a query spans multiple paper sections--often occurs when chunks are either too small (losing cross-paragraph context) or too "flat" (retrieving isolated snippets without their surrounding rationale).
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