Modern systems generate massive volumes of logs, yet most teams still rely on keyword searches and dashboards to understand production issues. This often leads to slow incident response and missed insights hidden within operational data.

In this session, we’ll explore how to turn logs into knowledge by combining Fluent Bit, OpenSearch, and Retrieval-Augmented Generation (RAG) to build a real-time, AI-assisted observability platform. We’ll start with designing an efficient log ingestion pipeline using Fluent Bit, covering parsing, enrichment, and filtering at scale. Next, we’ll examine how OpenSearch enables fast indexing, search, and analytics for operational logs.

Finally, we’ll demonstrate how RAG can be applied to logs, enabling natural-language queries such as “What caused the failure after the last deployment?” and “Summarize recurring errors across services.” Through real-world use cases, you’ll learn how AI-assisted observability can reduce mean time to resolution and transform reactive monitoring into knowledge-driven operations.