AI Observability Explained | Logs, Metrics, Traces & OpenTelemetry for AI Applications

Description

In this video, we break down AI Observability in simple terms and explain how developers monitor and debug modern AI systems. You will learn: • What observability means in AI applications • Why debugging AI systems is harder than traditional software • The three pillars of observability: Logs, Metrics, and Traces • How OpenTelemetry (OTel) works • How to monitor LLM calls, prompts, token usage, and latency • Observability for AI agents and RAG systems • Tools used by AI engineers for production systems