Knowledge base AI systems – how they really work | Decagon

This post details the technical underpinnings of AI knowledge bases, specifically how semantic search via vector embeddings, retrieval-augmented generation (RAG), and multi-source ingestion function. It outlines the four stages of RAG (embedding content, storing in vector DB, query matching, LLM generation) and the four-step automated content maintenance workflow (gap detection, stale content flagging, AI-assisted drafting, human review). It also highlights the importance of evaluating semantic search quality, automated content lifecycle, generative knowledge creation, analytics and gap detection, and integration depth when choosing a platform. The post positions Decagon's 'Suggestions' feature as an implementation of this automated maintenance workflow, drawing from conversations to identify gaps and generate drafts, and emphasizes integration with existing tools.