
Evaluating LLM Components in RAG Applications with DeepEval
This post demonstrates how to use DeepEval to systematically evaluate different components of a Retrieval-Augmented Generation (RAG) application, including retrievers, generators, and the full pipeline. It covers preparing golden datasets, applying relevant metrics like recall, precision, and faithfulness, and leveraging G-Eval for holistic application assessment.



