Verified claim · AI-ML · 100% confidence
Retrieval-Augmented Generation (RAG) introduced in paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020).
Last verified 2026-05-16 · Methodology veritas-v0.1 · d15057ced937a103
Structured fields
- Subject
- Retrieval-Augmented Generation (RAG)
- Predicate
introduced_in_paper- Object
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)
- Confidence
- 100%
- Tags
- rag · retrieval · foundational · lewis · 2020 · nips · facebook
Sources (2)
[1] preprint · arXiv (Lewis, Perez, Piktus, Petroni, Karpukhin, Goyal, Küttler, Lewis, Yih, Rocktäschel, Riedel, Kiela) · 2020-05-22
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks“We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever.”
[2] peer reviewed · NeurIPS Foundation · 2020-12-06
Retrieval-Augmented Generation (NeurIPS 2020 proceedings)
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curl https://sourcescore.org/api/v1/claims/d15057ced937a103.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/d15057ced937a103.json");
const envelope = await r.json();
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// "Retrieval-Augmented Generation (RAG) introduced in paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/d15057ced937a103.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Retrieval-Augmented Generation (RAG) introduced in paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_retrieval_augmented_generation_rag_fact() -> dict:
"""Fetch the verified SourceScore claim for Retrieval-Augmented Generation (RAG)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/d15057ced937a103.json")
return r.json()