May 17
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LLM observability is becoming essential for production AI, and this video breaks down how ObserveML tracks latency, token spend, hallucination signals, and RAG quality in one dashboard. This is an AI-hosted Deep Dive generated with Google NotebookLM, based on the ObserveML repository and project documentation. You’ll get a practical look at how to monitor whether an LLM system is actually working in production. In this AI-generated discussion, you'll hear: how ObserveML adds observability to LLM apps with a lightweight integration approach what teams can learn from metrics like p95/p99 latency, token cost, task completion, and anomaly alerts why RAG retrieval quality and prompt drift matter for real-world AI observability how hallucination signals and semantic analysis help catch failures beyond basic logs what makes ObserveML different from tools tied to a single framework or heavyweight MLOps stack Sources fed into NotebookLM: ObserveML GitHub repository: https://github.com/Venkatchavan/ObserveML-Production-Observability Audio generated using Google NotebookLM's Deep Dive format. All content is grounded in the uploaded sources. Resources & Sources: https://github.com/Venkatchavan/ObserveML-Production-Observability Contact: https://venkatchavan.github.io/portfolio_vc/ What part of production AI monitoring do you think most teams still ignore today? #AI #LLMObservability #RAG #NotebookLM