# Retrieval-Augmented 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.

- Identifier: PTL-0055
- Category: Retrieval & Tool Use
- Canonical URL: https://protologue.com/t/retrieval-augmented-generation/
- Also known as: RAG, retrieval augmentation, grounded generation
- Introduced: 2020

## 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.

## Narrower terms

- [Hypothetical Document Embeddings](https://protologue.com/t/hypothetical-document-embeddings/)
- [Self-RAG](https://protologue.com/t/self-rag/)

## Related terms

- [Hallucination](https://protologue.com/t/hallucination/)
- [Context Engineering](https://protologue.com/t/context-engineering/)

## Sources

- Lewis et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. https://arxiv.org/abs/2005.11401

## 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/

License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)