{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "@id": "https://anchorfact.org/kb/kb-2026-00006",
  "headline": "Retrieval-Augmented Generation (RAG)",
  "description": "Retrieval-Augmented Generation (RAG) is an AI architecture that combines large language models with external knowledge retrieval to produce more accurate, up-to-date, and source-attributed responses. Instead of relying solely on parametric knowledge (what the model memorized during training), RAG retrieves relevant documents from a knowledge base in real-time and provides them as context to the generation model. Google Scholar (2026) reports over 5,000 citations for the original RAG paper, reflecting its foundational role in modern AI systems.",
  "dateCreated": "2026-05-22T06:56:55.722Z",
  "dateModified": "2026-05-22T06:56:55.722Z",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": []
}