Leveraging AI for efficient incident response

This post details the development of an AI-assisted root cause analysis system for incident response. The system combines a heuristic-based retriever to reduce the search space of potential code changes and a Llama 2 LLM-based ranker to identify the top five most likely root causes. The LLM was fine-tuned using historical investigation data and a dedicated RCA SFT dataset. The system achieved 42% accuracy in identifying root causes at the investigation's creation time for the web monorepo. Future plans include autonomous workflow execution and proactive incident detection.