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How to Evaluate Your RAG System?

Mar 14, 2024 - vellum.ai
The article discusses the process of evaluating Retrieval Augmented Generation (RAG) systems, which are used to enhance output quality by retrieving relevant context from an external vector database. The evaluation process involves checking how effectively the system retrieves information from a knowledge base and uses it to produce reliable responses. The two most important parts to evaluate are context retrieval and content generation. However, other aspects such as business logic should also be evaluated.

The article also introduces Vellum, a tool that can be used to create custom evaluators for each step in the RAG system. Vellum's Evaluation Reports allow users to look at performance across various metrics. The tool can be used to set up all required RAG steps and evaluation mechanisms, deploy the system in production, and capture user feedback for further evaluation. The article emphasizes the importance of regular evaluation reports to ensure ongoing trust in the RAG system.

Key takeaways:

  • Retrieval Augmented Generation (RAG) systems require regular evaluation to ensure optimal performance. This involves testing the effectiveness of context retrieval, content generation, and business logic.
  • Context retrieval evaluation can be done using metrics such as context relevance, context adherence, and context recall. Content generation can be assessed based on answer relevancy, faithfulness, correctness, and semantic similarity.
  • Business logic evaluation should also be considered, with metrics varying based on the specific use-case and business needs.
  • Vellum provides tools for creating custom evaluators for every step in a RAG system, allowing for comprehensive performance assessment and continuous improvement.
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