A Non-Coder's Plain-English Breakdown of How RAG Pipelines Actually Work

A non-programmer IT professional explains Retrieval-Augmented Generation (RAG) after struggling to find accessible explanations that did not assume coding knowledge. RAG works by retrieving relevant text from a database before passing it to a language model, allowing the model to answer questions about documents it was never trained on. The process involves eight steps, including query clarification, vector conversion, dual-channel search, reranking, and prompt assembly before the model generates a response. The author uses an office analogy throughout — an executive assigning a task and a secretary pulling files — to make each technical step intuitive for non-technical readers. The piece aims to fill a gap in available resources, which tend to target developers rather than people who simply want to understand how the technology functions.