Retrieval-Augmented Generation
Also called RAG, retrieval augmentation, grounded generation.
Retrieval-augmented generation (RAG) supplies a language model with passages retrieved from an external corpus at query time, so its output is grounded in that information rather than only in its trained parameters.
Description
Lewis et al. introduced RAG as a jointly trained retriever and generator. In common usage the term now covers any pipeline that retrieves documents, typically by embedding search, and inserts them into the prompt. RAG reduces hallucination and allows knowledge to be updated without retraining.
Sources
- Lewis et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.
Cite this entry
Protologue. (2026). Retrieval-Augmented Generation. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0055). https://protologue.com/t/retrieval-augmented-generation/
BibTeX
@misc{protologue_retrieval_augmented_generation,
title = {Retrieval-Augmented Generation},
author = {{Protologue}},
year = {2026},
howpublished = {Protologue: A Taxonomy of Prompting and LLM Techniques, v1.0.0},
note = {Entry PTL-0055},
url = {https://protologue.com/t/retrieval-augmented-generation/}
}