A healthcare company is using Amazon Bedrock to build a Retrieval Augmented Generation (RAG) application that helps practitioners make clinical decisions. The application must achieve high accuracy…

Question

Options

• AUse Amazon Comprehend to analyze and classify RAG responses and to extract medical entities
• BImplement automated large language model (LLM)-based evaluations that use a specialized
• CConfigure Amazon CloudWatch Synthetics to generate test queries that have known answers on
• DDeploy a hybrid evaluation system that uses an automated LLM-as-a-judge evaluation to initially

• A
  25% (6)
• B
  17% (4)
• C
  8% (2)
• D
  50% (12)

Explanation

Option D is the correct solution because it directly addresses all three requirements: high retrieval accuracy, hallucination detection, and reduced human review costs. AWS recommends a layered evaluation strategy for high-stakes domains such as healthcare, where generative outputs must be both accurate and safe. Using an automated LLM-as-a-judge evaluation enables scalable, consistent assessment of generated responses for factual grounding, relevance, and hallucination risk. This automated screening significantly reduces the number of responses that require manual inspection. Only responses that fall below defined quality thresholds or exhibit ambiguous behavior are escalated to targeted human reviews, which optimizes review effort and cost. The use of Amazon Bedrock built-in evaluations provides standardized metrics specifically designed for RAG systems, including retrieval precision, faithfulness to source documents, and hallucination rates. These evaluations integrate directly with Amazon Bedrock knowledge bases and models, eliminating the need to build and maintain custom evaluation pipelines.

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