# Retrieval-Augmented Generation (RAG)

> Retrieval-augmented generation (RAG) is a method in which a language model retrieves relevant texts from an external source before answering and uses them in its answer. This grounds the answer in current or specific documents rather than training data alone.

- URL: https://techtafel.com/en/glossary/retrieval-augmented-generation/
- Published: 13 November 2026
- Other languages: DE https://techtafel.com/glossar/retrieval-augmented-generation/

**RAG combines search and text generation.** The method runs in two steps: first, a retriever searches an index for relevant passages. Then a language model writes the answer based on those passages.

The term was coined by a research team led by Patrick Lewis in May 2020 in the paper “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”. Their model drew on an index of Wikipedia passages and produced more specific and factually accurate answers.

AI search works on a similar principle. For AI Overviews and AI Mode, Google describes how the system retrieves pages from its index and generates an answer with links. For website owners this means:

- Content must be crawlable and indexed, otherwise no retriever can find it.
- Clearly structured, self-contained paragraphs are easier to retrieve as passages.

Our article on [AI Overviews and AI Mode](https://techtafel.com/en/blog/google-ai-overviews-ai-mode/) shows how this works at Google.

## Sources

1. [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401), Lewis et al., arXiv, 2020-05-22
2. [AI features and your website](https://developers.google.com/search/docs/appearance/ai-features), Google Search Central, 2025-12-10