This article introduces four key metrics for quantitatively evaluating Retrieval-Augmented Generation (RAG) systems, particularly for marketing chatbots. It breaks down RAG into two stages: retrieval and generation, with specific metrics for each. The Ragas library is presented as a tool to calculate these metrics, providing scores for context relevance, context recall, faithfulness, and answer relevance. The piece also details how to build and evolve a "golden set" of test cases to ensure diverse and robust evaluation. AI
IMPACT Provides a framework for improving the reliability and accuracy of RAG systems, crucial for enterprise AI applications.
RANK_REASON The article details a methodology and metrics for evaluating a specific type of AI system (RAG chatbots), akin to publishing research findings. [lever_c_demoted from research: ic=1 ai=1.0]
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