How do teams identify failure cases in production LLM systems?

LLM systems present unique challenges compared to traditional software, as they can fail in non-deterministic, context-dependent ways that are often silent and invisible until a user experiences an issue. Unlike traditional software errors, LLM failures may manifest as fluent yet incorrect responses, making it difficult to identify and prioritize them without a clear taxonomy of failure types, such as quality, safety, security, reliability, and cost failures. Effective detection of LLM failures requires a combination of proactive and reactive methods, including evaluation harnesses, shadow traffic comparisons, user feedback, anomaly detection, and business metric alerts. Key to addressing these failures is a comprehensive monitoring strategy that logs enough information to reconstruct reasoning paths without compromising privacy or security, as well as a robust triage workflow to pinpoint where failures occur within the complex LLM pipeline. By turning incidents into preventive measures, teams can create a cycle of improvement that enhances reliability and reduces the recurrence of similar issues, ultimately turning failure management into a strategic advantage.