# RAG (Retrieval-Augmented Generation)

> Source: https://rankxai.com/glossary/rag · Last updated: 2026-08-18

RAG, or retrieval-augmented generation, is the technique of fetching documents at question time and giving them to a language model to answer from, rather than relying on what the model learned in training. The 2020 paper that named RAG describes it as combining parametric and non-parametric memory.

## Where does the term RAG come from?

From [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401), submitted on 22 May 2020 by Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel and Douwe Kiela. The abstract defines the models as combining “pre-trained parametric and non-parametric memory for language generation”, where the parametric memory is the trained model and the non-parametric memory is a searchable index.

The paper’s index was Wikipedia. Five years on, the non-parametric memory is the live web, and the retrieval step is the one an assistant runs when it decides your page is worth reading. The architecture has changed a great deal since, and the vocabulary has not: parametric and non-parametric memory are still the two halves any honest description of an assistant’s answer has to separate.

## Why does the parametric and non-parametric split matter to a publisher?

Because it divides an assistant’s answer into the half you can influence this week and the half you cannot influence at all. The retrieved half is documents fetched now, so publishing a better page changes it as soon as the index catches up. The trained half was fixed at a knowledge cutoff months ago, and nothing you publish reaches it until the next model.

That split is also why no tool can honestly separate the two in a live answer. When an assistant names your brand, there is no field in the response saying whether the name came from a retrieved page or from training. Every product claiming to isolate training-data influence is estimating, and the estimate is unverifiable. It is also why the same brand can be described accurately by one assistant and be a year out of date in another on the same afternoon: one of them retrieved, and one of them remembered.

## Is “RAG” the same thing as an assistant searching the web?

Close enough for a marketer, and not close enough for an engineer. RAG names a family of architectures with a retriever and a generator; a consumer assistant with web access is one instance of it, wrapped in query rewriting, [fan-out](/glossary/query-fan-out), reranking and citation formatting that the original paper did not describe.

The reason to keep the technical definition anyway is that it tells you where to look when something is wrong. If your page is not in the index, no amount of writing helps; if it is retrieved and not used, the problem is the passage rather than the page.

## Sources

- [Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, 22 May 2020](https://arxiv.org/abs/2005.11401), checked 2026-08-18