# What is retrieval-augmented generation (RAG)?

> The plain-English definition, the 2020 paper that named it, and what it means for getting your business into AI answers.

Updated September 24, 2026 · AI Syndicate team · https://www.aisyndicate.com/glossary/retrieval-augmented-generation/

**Quick answer:** **Retrieval-augmented generation (RAG)** means an AI looks things up before it answers. First it *retrieves* relevant text from a search index or document store; then it *generates* an answer using that text. It lets AI answer with fresher, more specific facts than it memorised in training, and it is how AI search engines can cite sources.

## Where does the term RAG come from?

It comes from a 2020 paper by Patrick Lewis and colleagues, [*Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks*](https://arxiv.org/abs/2005.11401). NLP means natural language processing: getting computers to work with human language.

The paper's starting point: large language models store facts inside the model itself, but their ability to reach and use that knowledge precisely "is still limited." Its fix joined a model that writes text to a searchable index of Wikipedia, reached through a retriever. The authors report that this produced "more specific, diverse and factual language" than the same kind of model working from memory alone.

## How does RAG work in AI search?

Think of an open-book exam instead of a closed-book one.

1. **You ask a question.**
2. **Retrieve:** the system searches an index for passages that match.
3. **Augment:** it puts those passages in front of the model with your question.
4. **Generate:** the model writes an answer from them, and can link where they came from.

The big engines describe the same pattern in their own words. Google defines *grounding* as "providing content from the Google Search index to the model at prompt time to improve factuality and relevancy." [Google](https://developers.google.com/search/docs/crawling-indexing/google-common-crawlers) OpenAI runs OAI-SearchBot to surface sites in ChatGPT search, and ChatGPT-User to visit a page when a user's question calls for it. [OpenAI](https://developers.openai.com/api/docs/bots)

## Why does RAG matter for my business?

Because the retrieve step decides who can be in the answer. A page that was never fetched, or can't be read, can't be retrieved — and a page that isn't retrieved can't be cited.

- **Let the search bots in.** See [AI crawlers](/glossary/ai-crawlers/).
- **Answer the question in a short, self-contained passage.** In our experience, passages that make sense on their own are the ones that get lifted.
- **Keep facts current on your own site.** Retrieval works from indexed or freshly fetched pages, so a fix you publish can reach answers without waiting for a new model.

That last point is also why publishing the correct fact is the fix for an [AI hallucination about your brand](/glossary/ai-hallucination-brand/).

## FAQ

### Is RAG the same as training?

No. Training changes what the model has memorised and happens rarely. RAG hands the model fresh text at the moment it answers.

### Does RAG stop AI from making mistakes?

It helps, but it doesn't guarantee accuracy. If the retrieved page is wrong or out of date, the answer can be wrong too.

### Do ChatGPT and Google use RAG?

They describe the same idea. Google calls it grounding in the Google Search index, and OpenAI runs a search crawler and a user-triggered fetcher that supply pages to ChatGPT.

## Sources

1. [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)](https://arxiv.org/abs/2005.11401) — arXiv. Read Sep 24, 2026.
2. [Google's common crawlers](https://developers.google.com/search/docs/crawling-indexing/google-common-crawlers) — Google Search Central. Read Sep 24, 2026.
3. [Overview of OpenAI crawlers](https://developers.openai.com/api/docs/bots) — OpenAI. Read Sep 24, 2026.