A company runs a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock Knowledge Bases...

Option D is the correct solution because Amazon Bedrock Knowledge Bases natively support reranking by using Amazon-managed reranker models, which are specifically designed to improve contextual relevance after the initial vector retrieval step. This approach directly addresses the root cause of the issue: semantically similar but contextually irrelevant documents being passed to the foundation model.

By enabling the reranking configuration within Amazon Bedrock Knowledge Bases, the application can automatically reorder retrieved documents based on deeper contextual understanding, such as regulatory scope, legal applicability, and semantic intent. This significantly improves retrieval precision, which reduces hallucinations and improves the factual accuracy of generated regulatory guidance.

Option D requires no additional infrastructure, no custom orchestration logic, and no separate model hosting. The reranking is fully managed by Amazon Bedrock and integrates seamlessly with the existing RetrieveAndGenerateStream workflow. This makes it the lowest operational overhead solution.

Option A introduces operational complexity by requiring a custom SageMaker endpoint, API Gateway routing, and model lifecycle management. Option B combines multiple unrelated services and introduces significant complexity without being purpose-built for RAG relevance ranking. Option C improves relevance but requires explicitly calling the Rerank API and modifying the application pipeline, which increases operational and integration effort compared to built-in reranking.

Therefore, Option D provides the most efficient, scalable, and AWS-recommended method to improve RAG retrieval quality while minimizing operational burden.