{
  "id": "retrieval-augmented-generation",
  "code": "PTL-0055",
  "term": "Retrieval-Augmented Generation",
  "aliases": [
    "RAG",
    "retrieval augmentation",
    "grounded generation"
  ],
  "category": "retrieval-tools",
  "definition": "Retrieval-augmented generation (RAG) supplies a language model with passages retrieved from an external corpus at query time, so its output is grounded in that information rather than only in its trained parameters.",
  "description": "Lewis et al. introduced RAG as a jointly trained retriever and generator. In common usage the term now covers any pipeline that retrieves documents, typically by embedding search, and inserts them into the prompt. RAG reduces hallucination and allows knowledge to be updated without retraining.",
  "example": null,
  "broader": [],
  "narrower": [
    "hypothetical-document-embeddings",
    "self-rag"
  ],
  "related": [
    "hallucination",
    "context-engineering"
  ],
  "introduced": 2020,
  "sources": [
    {
      "title": "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks",
      "authors": "Lewis et al.",
      "year": 2020,
      "url": "https://arxiv.org/abs/2005.11401"
    }
  ],
  "url": "https://protologue.com/t/retrieval-augmented-generation/",
  "citation": "Protologue. (2026). Retrieval-Augmented Generation. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0055). https://protologue.com/t/retrieval-augmented-generation/"
}