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

According to DeepLearning.AI, its Retrieval Augmented Generation course emphasizes that building a reliable RAG system requires observability via LLM observability platforms that trace prompts through each pipeline step and support logging and evaluation. Source: DeepLearning.AI, Aug 16, 2025. For traders, this is an educational update rather than a product or partnership announcement, and the post provides no token mentions, financial metrics, or price guidance. Source: DeepLearning.AI, Aug 16, 2025.