According to @DeepLearningAI, production-ready RAG systems require robust observability across component-level and system-wide layers to monitor both system performance and output quality. Source: DeepLearning.AI on X 2026-01-19 https://twitter.com/DeepLearningAI/status/2013325617689719199 According to @DeepLearningAI, core evaluation coverage includes tracking latency and throughput and assessing response quality via human feedback or an LLM-as-a-judge. Source: DeepLearning.AI on X 2026-01-19 https://twitter.com/DeepLearningAI/status/2013325617689719199 According to @DeepLearningAI, the lesson details how to balance cost, automation, and accuracy when selecting evaluation metrics for an effective RAG observability framework. Source: DeepLearning.AI on X 2026-01-19 https://twitter.com/DeepLearningAI/status/2013325617689719199 and course page https://hubs.la/Q03_lM8f0 These evaluation practices are directly relevant to crypto market teams deploying AI agents and RAG-based research tools, where latency, throughput, and response quality metrics serve as reliability baselines and cost controls for production workflows. Source: DeepLearning.AI on X 2026-01-19 https://twitter.com/DeepLearningAI/status/2013325617689719199