LLM Evaluation & Observability in Production Retail Systems on GCP

Most teams know when their LLM is wrong after a customer complains. Production-grade retail AI requires knowing before that — with metrics, traces, and automated eval pipelines that catch drift, hallucination, and degradation continuously. This article shows you how to build that system on GCP. 🧭 Why LLM Observability in Retail Is Different Traditional ML observability tracks distribution drift on structured features and monitors a single scalar metric — accuracy, RMSE, AUC. LLMs break this model in three ways: Outputs are unstructured. There is no ground-truth label for "did the agent give a good answer?" arriving in real time. Failure modes are silent. A hallucinated return policy answer looks identical to a correct one in your latency dashboard. Context windows change behavior. The same model behaves differently depending on what is in the prompt — retrieved chunks, session history, tool results. In retail specifically, the stakes are asymmetric. A mis-personalized recommendation c