RAG (Retrieval-Augmented Generation)

An AI architecture pairing a large language model with external retrieval at query time — letting the model pull relevant source documents and ground its response in them rather than relying only on training memory. The architectural pattern behind every modern answer engine, and the foundation that makes Generative Engine Optimization possible. Addresses LLM limitations around factual currency, source attribution, and hallucination.

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