Observability Patterns for Production AI Systems: Monitoring RAG Pipelines, Vector Databases, and LLM Inference at Scale
The paper identifies five failure modes specific to production AI systems that traditional observability misses. It proposes an observability architecture integrating Prometheus, Grafana, and OpenObserve. Metrics are defined across retrieval quality, vector database health, LLM inference performance, and end-to-end pipeline latency. The framework was validated in a production environment handling 2 million daily queries. It reduced mean time to detection by up to 97% for previously undetectable incidents.