According to DeepLearning.AI, production-ready Retrieval-Augmented Generation (RAG) systems require robust observability to ensure both system performance and output quality. This involves monitoring latency and throughput metrics, as well as evaluating response quality using approaches like human feedback or large language model (LLM)-as-a-judge frameworks. Comprehensive observability enables organizations to identify bottlenecks, optimize component performance, and maintain consistent output quality, which is critical for deploying RAG solutions in enterprise AI applications. Strong observability also supports compliance, reliability, and user trust, making it a key factor for businesses seeking to leverage AI-driven knowledge retrieval and generation at scale (source: DeepLearning.AI on Twitter, August 6, 2025).