How to Evaluate RAG Performance on Amazon Bedrock Using LLM-as-a-Judge

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A technical deep-dive explores how to assess the performance of Retrieval-Augmented Generation (RAG) systems built on Amazon Bedrock Knowledge Bases. The evaluation uses the LLM-as-a-Judge framework, in which a large language model is used to score and assess the quality of RAG outputs. This approach provides a structured method for measuring how accurately and relevantly a RAG pipeline retrieves and generates information. The tutorial is aimed at developers and AI practitioners looking to benchmark and improve their RAG implementations on AWS.

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