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
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
Related verified claims
More verified claims related to this one — keep exploring.
Denoising Diffusion Probabilistic Models (DDPM) introduced in paper: Denoising Diffusion Probabilistic Models (Ho, Jain, Abbeel, 2020).
100% confidence · shares 3 tags (foundational, 2020, nips)
GPT-3 introduced in paper: Language Models are Few-Shot Learners (Brown et al., 2020).
100% confidence · shares 3 tags (foundational, 2020, nips)
ColBERT introduced in: Khattab & Zaharia 2020 — late-interaction retrieval.
100% confidence · shares 3 tags (retrieval, foundational, 2020)
Transformer architecture introduced in paper: Attention Is All You Need (Vaswani et al., 2017).
100% confidence · shares 2 tags (foundational, nips)
Reinforcement Learning from Human Feedback (RLHF) introduced in paper: Deep Reinforcement Learning from Human Preferences (Christiano et al., 2017).
100% confidence · shares 2 tags (foundational, nips)
Direct Preference Optimization (DPO) introduced in paper: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (Rafailov et al., 2023).
100% confidence · shares 2 tags (foundational, nips)
Chinchilla scaling laws introduced in paper: Training Compute-Optimal Large Language Models (Hoffmann et al., 2022).
100% confidence · shares 2 tags (foundational, nips)
Chain-of-Thought prompting introduced in paper: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022).
100% confidence · shares 2 tags (foundational, nips)
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)
Cite this claim
Ready-to-paste citation (Markdown / plain text):
Retrieval-Augmented Generation (RAG) introduced in paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020). — SourceScore Claim d15057ced937a103 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/d15057ced937a103.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the signed claim + primary source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/d15057ced937a103/" width="100%" height="360" frameborder="0" loading="lazy" title="Retrieval-Augmented Generation (RAG) introduced in paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)."></iframe>Preview: open in new tab
Frequently asked questions
Is the claim "Retrieval-Augmented Generation (RAG) introduced in paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)." verified?
Yes — SourceScore verified this claim with 100% confidence as of 2026-05-16. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.
What is the evidence for "Retrieval-Augmented Generation (RAG) introduced in paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)."?
Evidence comes from 2 primary sources: arXiv (Lewis, Perez, Piktus, Petroni, Karpukhin, Goyal, Küttler, Lewis, Yih, Rocktäschel, Riedel, Kiela), NeurIPS Foundation. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/d15057ced937a103.json includes an HMAC-SHA256 signature for audit verification.
When was this claim last verified by SourceScore?
Last verified 2026-05-16 under methodology version veritas-v0.1. The signed JSON envelope is dated and cryptographically signed for audit trail. Re-verification cadence depends on the claim type and source freshness.
How can I cite this SourceScore claim in my code or article?
Fetch the signed JSON envelope from https://sourcescore.org/api/v1/claims/d15057ced937a103.json which includes the verbatim claim, primary sources, confidence, methodology version, last-verified date, and HMAC-SHA256 signature for audit. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
Use this claim in your code
Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.
cURL
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();
console.log(envelope.claim.statement);
// "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()