Evaluating Retrieval-Augmented Generation Applications

Four Metrics for Ensuring Consistent Output Quality

Ongoing Health Checks for RAG (image by Sasun Bughdaryan)

Retrieval-Augmented Generation (RAG) is a natural language processing technique that combines elements of both retrieval-based and generative models. RAG empowers applications to deliver more precise, context-aware, and informative responses or content across a wide spectrum of use cases where natural language understanding and generation play pivotal roles.

(image by author)

Evaluating answer quality for RAG models is critically important:

• Information Accuracy: RAG models often retrieve information from external sources, which means the quality of the generated answer depends on the quality of the retrieved information. Inaccurate information can lead to misinformation and potential harm.
• Contextual Relevance: RAG models aim to generate contextually relevant answers by considering the context of the query or conversation. Evaluating answer quality assesses how well the model understands and respects the context, leading to more coherent and contextually appropriate responses.
• Completeness: Answer quality evaluation…