The verified answer is C. Use the LLM-as-a-judge approach with a rubric across prompts to compare model outputs. The question asks for a scalable way to compare two foundation models for chat summarization based on correctness and completeness. AWS documentation for Amazon Bedrock model evaluation states that an evaluation job using a judge model uses an LLM to score another model’s responses and provide an explanation of how it scored each prompt and response pair. This is specifically designed to compare model outputs across prompt datasets at scale, without requiring every output to be manually scored by humans.

AWS Bedrock documentation also lists built-in metrics for model evaluation jobs that use an LLM as a judge. The valid built-in metrics include Correctness and Completeness, which directly match the evaluation criteria in the question. Therefore, using LLM-as-a-judge with a rubric across prompts is the best fit because it evaluates the actual summaries produced by each model against the desired quality dimensions.

Option A is partially valid as an evaluation method, but it is not the most scalable approach. Human reviewers can provide high-quality judgments, but manual review becomes expensive and slow when comparing many prompts, outputs, and models. Option B is incorrect because latency and throughput measure operational performance, not whether a summary is correct or complete. A fast model can still produce incomplete or inaccurate summaries. Option D is incorrect because external benchmark rankings are generic. They do not prove that a model performs best on the company’s specific chat summarization workload.

The decisive terms are “scalable,” “correctness,” and “completeness.” Amazon Bedrock LLM-as-a-judge evaluation directly supports those dimensions, so option C is correct.