How to evaluate RAG systems: metrics, frameworks & infrastructure

Retrieval Augmented Generation (RAG) systems, which integrate large language models (LLMs) with external information sources to generate accurate and current responses, often face challenges in production environments that are not visible during demonstrations. Evaluating RAG systems involves assessing performance across several stages—chunking, retrieval, reranking, context assembly, and generation—by focusing on three core dimensions: context relevance, groundedness (faithfulness), and answer relevance. These evaluations are crucial because failures at any stage can cause cascading errors, leading to irrelevant or hallucinated answers. Automated evaluation frameworks facilitate consistent scoring across large query volumes, allowing for efficient monitoring and optimization of RAG systems at scale. By integrating evaluation into the CI/CD pipeline, developers can catch quality regressions early, preventing degradation before reaching end-users. Redis provides an integrated infrastructure to support the evaluation process, enabling efficient handling of production-scale workloads and tracking quality trends over time.