How to Build a Reliable RAG Pipeline Evaluation Framework with Key Metrics

Teams building Retrieval-Augmented Generation (RAG) systems often underinvest in evaluation, risking undetected failures in both retrieval and generation stages. A robust test harness relies on four core metrics: context recall, context precision, faithfulness, and answer relevance. The foundation is a curated dataset of question-answer-context triplets, with even 50 well-chosen samples sufficient to catch most regressions. Faithfulness — whether generated answers stick to retrieved context — is best measured using a language model as a judge, since string matching cannot capture semantic nuance. A faithfulness score below 0.8 often signals a retrieval problem that manifests as apparent model hallucination, underscoring why independent evaluation of each pipeline stage matters.