After deploying a Retrieval-Augmented Generation system, the focus shifts to sustaining its performance and reliability in a live setting. It's not enough to simply launch; continuous assessment and management are necessary to ensure the system remains effective over time. This chapter details the methods and practical techniques for thorough evaluation and diligent monitoring of RAG systems as they operate in production.
You will learn to implement comprehensive evaluation frameworks, including established tools like RAGAS and ARES, alongside developing custom metrics. We will cover the distinctions and applications of offline versus online evaluation strategies, and how to build automated pipelines for ongoing assessment. The chapter also addresses critical aspects such as monitoring for data and concept drift in retrieval components, tracking LLM performance degradation, and integrating user feedback for continuous refinement. Additionally, you will explore A/B testing for optimizing configurations and learn to construct system health dashboards for clear visibility into your RAG system's operational status and key performance indicators.
6.1 Advanced RAG Evaluation Frameworks (RAGAS, ARES)
6.2 Offline vs. Online Evaluation Strategies
6.3 Automated Evaluation Pipelines
6.4 Monitoring Drift in Retrieval Components
6.5 Monitoring LLM Performance in RAG Systems
6.6 Integrating User Feedback for RAG Refinement
6.7 A/B Testing Strategies for RAG Optimization
6.8 Building RAG System Health Dashboards
6.9 Hands-on: Implementing a RAG Monitoring Dashboard