LLM observability is the practice of monitoring, measuring, and understanding the behavior of large language models in production environments, enabling teams to track quality, performance, cost, and security across AI applications. Unlike traditional software observability, LLM observability must capture the non-deterministic nature of generative AI—tracking prompt inputs, model outputs, token usage, latency, hallucinations, and user feedback across complex multi-step workflows. As LLMs power increasingly critical business applications in 2026, observability has shifted from a nice-to-have debugging tool to production infrastructure essential for reliability, compliance, and cost control. The key mental model: treat LLM observability as distributed tracing for AI—every request becomes a trace with nested spans capturing retrieval, reasoning, generation, and tool calls, with quality metrics evaluated at each step before responses reach users.