How We Use RAG for Knowledge Base Search in AutoBot

How We Use RAG for Knowledge Base Search in AutoBot Part 2: Unlocking Your Team's Collective Intelligence In Part 1 , you set up AutoBot and experienced how it can execute basic infrastructure tasks. Now let's unlock its real power: turning your scattered knowledge into instant, intelligent answers . Where does your team's critical knowledge live? Deployment runbooks in Google Drive. Database failover procedures in forgotten Confluence docs. Incident post-mortems buried in Slack. At 3 AM during an outage, finding that knowledge is nearly impossible. AutoBot solves this with Retrieval-Augmented Generation (RAG) —a technique that lets AutoBot search your actual documentation and generate answers based on your procedures, not generic training data. We'll explore how RAG works, build a practical knowledge base, and show you why this beats traditional keyword search. What Is RAG? (Plain English) RAG stands for Retrieval-Augmented Generation —three operations in one: Retrieval : Find relevan