# Retrieval-Augmented Generation (RAG)

> Fetching relevant documents at query time and giving them to the model as context, so answers are grounded in current, specific data.

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- **term:** Retrieval-Augmented Generation (RAG)
- **category:** knowledge-memory
- **short_def:** Fetching relevant documents at query time and giving them to the model as context, so answers are grounded in current, specific data.
- **long_def:** The term was coined by Patrick Lewis and colleagues at Facebook AI Research (now Meta AI), University College London and NYU in a 2020 NeurIPS paper. RAG reduces hallucination and lets a model answer about information it was never trained on. Agent-friendly sites help RAG by exposing clean, chunkable content (markdown twins, llms.txt) and structured data.
- **see_also:** grounding, markdown-twin
- **etymology_origin:** Coined by Patrick Lewis et al. in 'Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks' (Facebook AI Research / UCL / NYU), arXiv 2005.11401, May 2020; presented at NeurIPS 2020.
- **related_to:** grounding, markdown-twin, json-ld
- **contrast_with:** Unlike fine-tuning, which bakes knowledge into model weights, RAG fetches documents at query time and supplies them as context — knowledge stays external, current and citable.
- **example:** The seminal 2020 RAG paper (Lewis et al., arXiv 2005.11401) combined dense-passage retrieval with a BART generator and set state-of-the-art on three open-domain QA benchmarks.
- **source:** https://arxiv.org/abs/2005.11401
- **status:** active
- **why_it_matters:** RAG is how most AI answer engines ground responses; sites that expose clean, retrievable content get pulled into and cited by those answers.
- **sameAs:** https://en.wikipedia.org/wiki/Retrieval-augmented_generation
- **bridge_entity:** models
- **last_verified:** 2026-06-15
- **md_twin:** /glossary/rag.md