Retrieval-augmented generation (RAG) enhances large language models by connecting them to dynamic and specialized data, but its implementation can be challenging. Thorough evaluation is crucial to avoid 'silent failures' that undermine system reliability. Best practices include establishing a rigorous, automated testing framework, selecting appropriate evaluation metrics, and combining quantitative and qualitative testing. Tools like Ragas and Google Cloud's Vertex AI Gen AI evaluation service can assist in optimizing RAG systems.
Table of contents
Common RAG evaluation frameworks include:Example metricsStep 2. Root cause analysis and iterative testingExamples of RAG experiments to runStep 3. Human evaluationConclusionSort: