Quick answer: Trace, debug, and monitor LLM apps in production — costs, latency, quality.
LLM Observability is the practice of instrumenting, monitoring, and debugging Large Language Model applications in production. It gives you visibility into what your LLM is doing—what prompts it receives, what outputs it generates, how long requests take, what they cost, and where failures occur. Think of it as adding eyes and ears to your AI system. With observability tools, you can trace a user's request through your entire LLM pipeline, spot bottlenecks in latency, identify which API calls are burning through your budget, detect when model outputs degrade in quality, and pinpoint exactly which prompt or configuration change broke something. For example, you might discover that 40% of your chatbot's requests timeout during peak hours, or that a particular user segment gets consistently poor responses. Observability transforms LLM development from guesswork into data-driven debugging.