LLM Observability in Production: The Four Silent Failures Engineers Miss
· 9 min read
Most teams shipping LLM applications to production have a logging setup they mistake for observability. They store prompts and responses in a database, track token counts in a spreadsheet, and set up latency alerts in Datadog. Then a user reports the chatbot gave wrong answers for two days, and nobody can tell you why — because none of the data collected tells you whether the model was actually right.
Traditional monitoring answers "is the system up and how fast is it?" LLM observability answers a harder question: "is the system doing what it's supposed to do, and when did it stop?" That distinction matters enormously when your system's behavior is probabilistic, context-dependent, and often wrong in ways that don't trigger any alert.
