According to DeepLearningAI, production-ready Retrieval Augmented Generation (RAG) systems require comprehensive observability to ensure reliable performance and output quality (source: DeepLearningAI on Twitter, Jan 19, 2026). Effective observability involves monitoring both latency and throughput, as well as evaluating response quality using human feedback or LLM-as-a-judge methods. DeepLearningAI's course highlights that a robust evaluation system is essential for identifying issues at both component and system-wide levels. The lesson emphasizes balancing cost, automation, and accuracy when selecting metrics for AI system monitoring. This approach enables AI teams to deploy RAG solutions with confidence, reduces operational risks, and helps businesses maintain high-quality AI-driven outputs, creating tangible business opportunities in regulated and mission-critical industries (source: DeepLearningAI, https://hubs.la/Q03_lM8f0).