How a RAG System Splits Into Knowledge and Query Pipelines Explained

A developer tutorial series on building a Retrieval-Augmented Generation (RAG) system explains how its components organize into two distinct pipelines. The knowledge pipeline handles ingesting, processing, and indexing raw information from sources like Notion or Slack so it can be retrieved later. The query pipeline takes a user's question, retrieves relevant stored knowledge, and passes it as context to a large language model to generate an answer. The separation mirrors how search engines like Google crawl and index content separately from serving search results. The article dives deeper into connector and ingestion layer roles, showing how source-specific connectors fetch and structure raw records before they enter the broader pipeline.