Effective cross-lingual LLM evaluation with Amazon Bedrock

Evaluating the quality of AI responses across multiple languages presents significant challenges for organizations deploying generative AI solutions globally. How can you maintain consistent performance when human evaluations require substantial resources, especially across diverse languages? Many companies find themselves struggling to scale their evaluation processes without compromising quality or breaking their budgets.

Amazon Bedrock Evaluations offers an efficient solution through its LLM-as-a-judge capability, so you can assess AI outputs consistently across linguistic barriers. This approach reduces the time and resources typically required for multilingual evaluations while maintaining high-quality standards.

In this post, we demonstrate how to use the evaluation features of Amazon Bedrock to deliver reliable results across language barriers without the need for localized prompts or custom infrastructure. Through comprehensive testing and analysis, we share practical strategies to help reduce the cost and complexity of multilingual evaluation while maintaining high standards across global large language model (LLM) deployments.

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