When a chatbot sells a Chevrolet for one dollar: how to test and monitor LLM applications

▤ Summary

This article explains how to test and monitor LLM applications in production. It covers evaluation methods such as golden references, A/B testing, offline and online checks, and LLM-as-a-judge approaches. It also discusses guardrails, prompt injection risks, hallucination detection, semantic similarity, and metrics like precision, recall, hit rate, and NDCG. The piece emphasizes building actionable monitoring for LLM workflows, including RAG, JSON output validation, and drift detection. The focus is on practical methods and tooling for teams deploying AI applications.

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