Metrics-Driven Development of RAGs

Jithin James and Shahul Es shared insights on leveraging metrics-driven development to evaluate Retrieval Augmented Generation (RAG) systems at the Zilliz Unstructured Data Meetup. They discussed both the theoretical foundations and practical applications of RAG system evaluation, explaining how understanding the theory behind the evaluation code can provide deeper insights into its functionality. The talk covered key metrics such as factual similarity and semantic similarity to determine the quality and relevance of the generated answer compared to the ground truth. Practical examples were provided on how to evaluate and improve a RAG system powered by Milvus, an open-source vector database known for its efficiency in similarity search and AI applications.