A Practical Framework for Evaluating Whether Your RAG System Actually Works

Evaluating a Retrieval-Augmented Generation (RAG) application requires more than a few successful test answers, as the retriever and generator can each fail independently or together. Developers are advised to build a fixed 'golden set' of representative questions, expected answers, and relevant document chunks to serve as a consistent benchmark across versions. The evaluation framework breaks testing into distinct layers: component-level checks on the retriever and generator separately, followed by pipeline and full application assessments. Key retriever metrics like recall help identify whether relevant information even reaches the model before generation is assessed. Maintaining the same evaluation set across versions is critical, as changing it to improve scores undermines the validity of any comparison.