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A practical explainer on RAG: the six stages of a retrieval pipeline, how it compares with prompt-only generation and keyword search, common applications, limitations, and a framework for deciding whether to build one.
RAG splits the job in two: a retrieval step finds passages relevant to the question, then the model answers using those passages as context. It is the standard fix for knowledge cutoffs, private data, and hallucination, because the model is grounded in text it can cite. Retrieval quality sets the ceiling — the best model cannot rescue the wrong three paragraphs. In practice: A support bot that quotes your actual refund policy rather than a plausible-sounding invention. ...