LLM observability is a critical practice in managing the complex, non-deterministic nature of Large Language Models (LLMs) in production environments. It involves collecting telemetry data to assess and enhance system performance by monitoring prompts, user feedback, latency, API usage, and retrieval performance. As AI-powered applications like chatbots and translation services increasingly rely on LLMs, the need for observability grows due to the models' unpredictability and resource demands. The practice goes beyond traditional software observability by addressing the unique challenges of LLMs, such as their stochastic nature and context-driven outputs, which traditional testing methods cannot predict. Observability aids in root cause analysis, performance bottleneck identification, output assessment, pattern detection in responses, and developing guardrails for LLM applications. Various tools and platforms have emerged to support LLM observability, offering features like prompt management, tracing, evaluations, and retrieval analysis, helping developers and operators gain deeper insights into application behavior and improve user experience.