What is retrieval-augmented generation (RAG)?
Retrieval-augmented generation (RAG) is a method where a language model first retrieves relevant documents and then writes its answer with them in view. Every AI engine that cites sources runs some form of RAG: look things up, then write with the results in view. For a brand, the retrieval step is the gate. A page that is never retrieved cannot shape the answer, however good it is.
Where does retrieval-augmented generation come from?
The name comes from Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks by Patrick Lewis and colleagues, submitted May 22, 2020 and published at NeurIPS 2020. It combined a model's parametric memory, what it learned in training, with a non-parametric memory: a dense vector index of Wikipedia that a retriever searched for each question.
AI search engines use the same idea at web scale, with a search index in place of the Wikipedia index and live results in place of a fixed snapshot.
How does RAG work in AI search?
In AI search, RAG runs in a few steps:
- The engine rewrites the question into one or more searches.
- It retrieves results from a web index, its own or a partner's.
- It picks which results to pass to the model.
- The model writes the answer with those results in its context.
- The engine attaches citations to some of the sources it used.
Is every retrieved page cited?
No. OpenAI documents that the sources field of a web search returns every URL the model consulted, and that the number of sources is often greater than the number of citations (OpenAI API docs, read Oct 15, 2026).
So a citation count understates what an engine read, and a page can inform an answer without a link (the post). Retrieval still pays: in our data, 94 of 97 answers that cited a page from the brand's own site also named the brand (the post).
What does retrieval look like in a real example?
In the public Tesla example (Oct 4, 2026), what the engines retrieved and cited differed by engine. ChatGPT cited tesla.com 12 times in 93 citations across 25 buyer questions, about 13%. Perplexity, Claude and Google AI Overviews together cited it 5 times in 915.
The rest of the retrieval set was other people's pages: across buyer and brand questions, youtube.com was cited 29 times, en.wikipedia.org 24 and reddit.com 20. Those are the pages the model wrote from.
Common questions about Retrieval-augmented generation (RAG)
Does RAG mean the AI only uses what it retrieved?
No. The model still brings what it learned in training, and the answer blends the two. Retrieval steers the answer; it does not replace the model's memory.
How do I get my pages retrieved?
Let AI search crawlers read them, make the text present without JavaScript, keep them indexed and snippet-eligible, and answer the specific sub-questions buyers ask.
Is RAG the same as grounding?
They overlap. RAG is the method of retrieving documents before writing; grounding is the goal of tying the answer's claims to those sources.
Why was my page cited but my brand not named?
Your page informed the answer but the shortlist went elsewhere. It usually means the page answers a sub-question well but does not make the case for you.
Related terms
- Grounding
Grounding is tying an AI model's answer to outside sources, such as live search results, so its claims rest on retrieved content rather than on memory alone.
- Training data vs live search
Training data is what a model learned before its knowledge cutoff; live search is what it looks up on the web while it answers, and one AI answer can draw on either or both.
- Query fan-out
Query fan-out is when an AI engine splits one question into several related searches and builds its answer from all the results.
- AI citation
A citation is a web page an AI engine links to or lists as a source for its answer.
Worked example: Tesla, public AI answers, Oct 4, 2026: Proofsource's first scan of Tesla, 25 buyer questions and 5 brand questions, one answer per question on each of four engines. A small sample from one brand, shown as an example, not a benchmark.
See where AI leaves you out, and what to fix.
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