What Should an AI Incident Postmortem Look Like? A Guide for Teams Running LLM Systems in Production

When dealing with incidents involving traditional software systems, postmortems typically follow a well-established process, focusing on identifying root causes, contributing factors, and actionable improvements. However, the emergence of AI systems, especially those powered by Large Language Models (LLMs), introduces a new layer of complexity that challenges these conventional approaches. The core issue lies in the inherent unpredictability of AI; unlike deterministic code, LLMs can produce unexpected or even "hallucinated" outputs without clear error signals, making diagnosis and remediation significantly more difficult. This guide from Sherlocks.ai addresses the critical need for specialized incident postmortem practices tailored to AI systems in production. It argues that a "normal" postmortem, which assumes a predictable system, is insufficient for understanding and preventing future AI-related failures. Instead, teams operating LLM systems require a framework that accounts for the unique characteristics of artificial intelligence. The article proposes an "AI Postmortem Extension," which includes six essential fields and a template designed to capture the nuances of AI incidents. These fields likely delve into aspects such as model behavior, data drift, prompt engineering effectiveness, and the specific ways in which the AI system deviated from expected performance. The goal of this specialized postmortem approach is to move beyond simply identifying technical failures to understanding the complex interplay between data, model, and deployment environment that can lead to AI incidents. By systematically reviewing these incidents with an AI-centric lens, organizations can develop more effective strategies for monitoring, debugging, and improving the reliability of their LLM-powered applications. This includes refining model training, enhancing data pipelines, and implementing better guardrails to mitigate risks associated with AI unpredictability. Ultimately, the guide aims to foster a culture of continuous learning and improvement, ensuring that teams are better equipped to handle the unique challenges of operating advanced AI systems in real-world scenarios, leading to more resilient and trustworthy AI deployments.