Explanation:
Option A is the correct solution because hybrid search directly addresses the core retrieval failure modes while maintaining low latency and minimal operational overhead. In medical and scientific domains, exact terminology, abbreviations, and acronyms (for example, drug names, procedures, or conditions) are critical. Pure vector similarity search often underweights these exact matches, leading to missed results and excessive semantically related but irrelevant documents.
Amazon OpenSearch Service natively supports hybrid search, which combines keyword-based retrieval (such as BM25) with vector similarity search. Keyword search ensures precise matching for exact terms and acronyms, while vector search captures semantic meaning and contextual similarity. By blending these approaches, the retrieval system improves both precision and recall without introducing additional infrastructure.
Hybrid search operates within the same OpenSearch index and query path, which preserves low end-user latency even at large scale. This is especially important as the document collection grows to millions of documents. Because OpenSearch handles scoring and ranking internally, no additional orchestration layers or post-processing steps are required.
Why other options are less optimal:
Therefore, Option A delivers the best balance of retrieval quality, scalability, latency, and operational simplicity for medical RAG workloads.
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A medical company is building a generative AI (GenAI) application that uses Retrieval Augmented Generation (RAG) to provide evidence-based medical information. The application uses Amazon OpenSearch Service to retrieve vector embeddings. Users report that searches frequently miss results that contain exact medical terms and acronyms and return too many semantically similar but irrelevant documents. The company needs to improve retrieval quality and maintain low end-user latency, even as the document collection grows to millions of documents.
Which solution will meet these requirements with the LEAST operational overhead?
A
Configure hybrid search by combining vector similarity with keyword matching to improve semantic understanding and exact term and acronym matching.
B
Increase the dimensions of the vector embeddings from 384 to 1536. Use a post-processing AWS Lambda function to filter out irrelevant results after retrieval.
C
Replace OpenSearch Service with Amazon Kendra. Use query expansion to handle medical acronyms and terminology variants during pre-processing.
D
Implement a two-stage retrieval architecture in which initial vector search results are re-ranked by an ML model hosted on Amazon SageMaker.