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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.
Vector search embeds the query and returns the nearest stored vectors, so ‘how do I get my money back’ finds a refund policy that never uses those words. It is the retrieval engine behind RAG. It is also weaker than keyword search on exact identifiers, which is why hybrid search exists. In practice: A query with no shared vocabulary still surfaces the right document.