RAG
Also called Retrieval-augmented generation
Retrieval-augmented generation combines a generative model with retrieved external information. The retrieved material helps inform the response instead of relying only on information encoded in the model’s parameters.
[Lewis et al.]In practice · hypothetical example
A museum assistant retrieves a catalog entry, then uses that entry to draft a visitor-facing answer about a painting.
[Lewis et al.]A little deeper
The original RAG research combines a pretrained generator with a retriever over an external index. Its reported improvements are experimental results, not a guarantee that every retrieved or generated claim is correct. [Lewis et al.]
A common mix-up
Using RAG guarantees a correct answer.
The retrieved evidence and the generated answer still need checking. [Lewis et al.]
Similar terms, different emphasis
Compare RAG & Fine-tuning →An assistant retrieves a catalog entry before drafting an answer. Which pattern is this?
Sources & editorial notes
Evidence: supported. Primary-source support for this scoped entry; publication approved by the project owner.
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks ↗ (opens in new tab)Lewis et al. · Publication date unknown
Relevant section: Abstract
Last editorial review: 2026-09-13 by project-owner.
First observed in this corpus: Unknown.
Revision history
Revision 2 · Created 2026-09-13 · Updated 2026-09-13
Project owner approved the current content for publication. Existing evidence scope and limitations remain applicable.