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.

RAG is how most AI answer engines ground responses; sites that expose clean, retrievable content get pulled into and cited by those answers.

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

last verified · by

← all The Agentic Web Lexicon · .md · JSON