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Technical implementation · AI Search Infrastructure

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Definition

RAG — Retrieval-Augmented Generation — is an AI architecture that combines a language model with a real-time retrieval system. When a query is submitted, the system retrieves relevant documents from an external knowledge source, then uses those documents as context for generating a grounded, cited response. RAG is the primary mechanism by which AI search systems produce factually anchored answers with source citations. RAG is the architecture that makes AI citations possible. Without RAG, AI systems generate responses purely from training data — producing answers that may be outdated or hallucinated. With RAG, the system retrieves current, specific content to ground its answer. For brands, RAG means that content structure, indexability, and semantic clarity directly affect whether the brand’s content is retrieved and cited. RAG systems are actively looking for content to pull — brands that make their content retrievable benefit; brands that don’t are invisible to the retrieval layer.

Retrieval pipeline

Dense retrieval

Vector database

Grounding

Hallucination mitigation

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