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).

According to Andrew Ng (@AndrewYNg), the single most important factor influencing the speed of progress in building AI agents is a team's ability to implement disciplined processes for evaluations (evals) and error analysis. Ng emphasizes that while it might be tempting to quickly address surface-level mistakes, a structured approach to measuring system performance and identifying root causes of errors leads to significantly faster, more sustainable progress in developing agentic AI systems. He notes that traditional supervised learning offers standard metrics like accuracy and F1, but generative and agentic AI systems pose new challenges due to a much wider range of possible errors. The recommended best practice is to prototype quickly, manually inspect outputs, and iteratively refine both datasets and evaluation metrics—including using LLMs as judges where appropriate. This approach enables teams to precisely measure improvements and better target development efforts, which is crucial for enterprise AI adoption and scaling. These insights are shared in depth in Module 4 of the Agentic AI course on deeplearning.ai (source: Andrew Ng, deeplearning.ai/the-batch/issue-323/).