Question
A medical company is building a generative AI (GenAI) application that uses 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. Correct
- 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 that is hosted on Amazon SageMaker AI.
Explanation
Option A is correct because it suggests configuring hybrid search, which combines vector similarity with keyword matching. This approach improves the retrieval of exact medical terms and acronyms while maintaining semantic understanding, thus addressing the issue of missing relevant results and reducing irrelevant document returns. Additionally, this solution maintains low operational overhead compared to other options that involve more complex architectures or services.
