A company is building a legal research AI assistant that uses Amazon Bedrock with an Anthropic Claude foundation model (FM). The AI assistant must retrieve highly relevant case law documents to…

Question

Options

• AConfigure an Amazon Bedrock knowledge base to use a default vector search configuration. Use
• BUse Amazon OpenSearch Service to deploy a hybrid search architecture that combines vector
• CEnable the Amazon Kendra query suggestion feature for end users. Use Amazon Bedrock to
• DUse Amazon OpenSearch Service with vector search and Amazon Bedrock Titan Embeddings to

• A
  7% (2)
• B
  77% (23)
• C
  3% (1)
• D
  13% (4)

Explanation

Option B is the correct solution because legal research workloads require both semantic understanding and exact lexical precision, especially for statutes, citations, and domain-specific terminology. A hybrid search architecture directly addresses this need by combining vector similarity search with traditional keyword-based retrieval. Vector search alone is often insufficient for legal research because exact phrases, citation formats, and jurisdiction-specific terms must be matched precisely. Keyword search ensures high recall and precision for citations and legal terms, while vector search captures deeper semantic relationships between legal concepts, precedents, and arguments. Amazon OpenSearch Service natively supports hybrid search, enabling efficient scoring and ranking without external Applying an Amazon Bedrock reranker model further improves relevance by reordering retrieved documents based on deeper contextual understanding. Reranking is especially valuable in legal research because multiple documents may appear relevant, but only a subset truly addresses the user's legal question. The reranker optimizes final results before they are passed to the Anthropic Claude FM, improving answer accuracy and reducing hallucinations.

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