Leveraging AI for efficient incident response

What this post added

This post details the development and implementation of an AI-assisted root cause analysis system for incident response. The system combines heuristic-based retrieval to narrow down potential code changes and a Llama-based LLM ranker to identify the most likely root causes. The training process involved continued pre-training and supervised fine-tuning of a Llama 2 (7B) model using historical investigation data and internal documentation. The system achieved 42% accuracy in identifying root causes at the creation time of investigations related to the web monorepo. Future plans include expanding capabilities to autonomous workflow execution and proactive incident detection.