In today’s distributed microservices landscape, Kubernetes environments generate vast volumes of logs, making troubleshooting complex and time-consuming. Operators often sift through massive data to identify issues, leading to prolonged downtime—a challenge that intensifies with multiple clusters. Discover how GenAI optimizes troubleshooting by transforming traditional logs into conversational insights. This session covers building an AI-driven observability solution with Large Language Models (LLMs). We start by configuring Fluent Bit collectors to gather systemd logs, Kubernetes events, and application logs, which are then streamed to a scalable object storage. By constructing a vector database, we enable users to query and interact with logs in natural language. We will provide a step-by-step guide that equips attendees with actionable knowledge to implement GenAI observability in their Kubernetes clusters.