How RAG Lets AI Answer Questions From Large Document Sets Without Retraining

The article explains Retrieval-Augmented Generation, a method that lets AI answer questions from long documents without being retrained. Using an HR example, it describes a company wanting to know its vacation carry-over rules buried in a 600-page handbook. RAG works by first retrieving relevant passages and then having the language model answer based on that evidence. The system is split into an indexing pipeline that ingests, chunks, and embeds documents, and an answering pipeline that retrieves, checks authorization, and generates cited responses. The author stresses that retrieval must be reliable, since similarity alone does not guarantee a correct passage.