# What Is RAG? Retrieval-Augmented Generation Explained **Definition:** Retrieval-augmented generation (RAG) is a technique where an AI model first searches a set of documents for passages that match a question, then writes its answer from those passages. Because it looks things up at answer time, the model can use up-to-date information and point to where it came from. **Published:** September 30, 2026 **Author:** Connor Lahey --- ## How RAG works Retrieval-augmented generation runs every time someone asks a question, and it has three stages. 1. **Retrieve.** The system searches a document collection for the passages most relevant to the question. The collection might be a company knowledge base, a pile of product manuals, or the web. Many systems match by meaning using [embeddings](https://www.searchable.com/glossary/embeddings), often alongside ordinary keyword search. 2. **Augment.** The best passages go into the prompt next to the original question, usually with an instruction like "answer using these sources." 3. **Generate.** The language model writes its answer from that longer prompt. Many systems also add citations pointing back to the passages they used. Before any of this can happen, documents get split into smaller pieces. That step is [content chunking](https://www.searchable.com/glossary/content-chunking), and it's the reason AI engines tend to quote a paragraph or a section instead of a whole page. Chunking is also how RAG handles long documents: a model can only read a limited amount of text at once (its [context window](https://www.searchable.com/glossary#context-window)), so only the most relevant chunks go into the prompt. The name comes from a 2020 paper, "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks," by Patrick Lewis and colleagues at Facebook AI Research, presented at NeurIPS 2020. They connected a language model to a searchable index of Wikipedia and found the combined system wrote more specific and more factual answers than the model did on its own. ## Why RAG exists Language models have two limits that retrieval works around. Their knowledge stops at a date. A model only knows what was in its training data, up to its [knowledge cutoff](https://www.searchable.com/glossary#knowledge-cutoff), so anything published later has to be fetched. They also know nothing private. A model trained on public text has never seen your pricing sheet or your support docs, and retrieval gives it access to them without retraining the model. There's a third benefit: answers become checkable. When a model cites the passage it used, a reader can open the source and confirm the claim. That link between an answer and its source is called [grounding](https://www.searchable.com/glossary/grounding). ## RAG vs a plain LLM | | Plain LLM | RAG system | |---|---|---| | Source of knowledge | Training data only | Training data plus retrieved documents | | Freshness | Fixed at the knowledge cutoff | As current as the document collection | | Citations | None, or unreliable | Can link to the passages it used | | Private data | Not available | Available if it's in the collection | The model can be identical in both columns. What changes is the material in front of it when it writes. ## RAG vs fine-tuning Both adapt a model to a job, and they change different parts of the system. Fine-tuning retrains the model on extra examples, so the new behavior lives in the model's weights. It suits teaching a tone or a fixed output format, and updating what the model knows means training it again. RAG leaves the model alone and supplies facts when the question arrives. It suits information that changes often or needs a source attached, and updating it means editing the documents. Plenty of production systems use both, with a fine-tuned model handling tone and format and retrieval handling the facts. ## RAG and hallucinations Retrieval lowers the rate of AI hallucinations because the model has real text to work from, though some still get through. A model can misread a passage, merge details from two sources, or add something no source said. And if retrieval returns an outdated or inaccurate page, the answer inherits the mistake. This explains a lot of the wrong facts brands find in AI answers. The engine often did retrieve a real page; that page was just out of date, or described the product badly. ## What RAG means for brands AI search engines work like RAG systems running at web scale. Perplexity searches the web in real time when you ask a question. ChatGPT searches when it decides current information would help, and you can also turn search on yourself. Google says AI Overviews and AI Mode may run several related searches (query fan-out) and show supporting links from the web. In every case, an answer can only cite pages the system retrieved first. For your content, that comes down to a few practical checks: - **Can it be found?** The page has to be crawlable and indexed, and it has to match the searches the engine runs, including the related queries it generates from one question. - **Does each section stand on its own?** A clear heading with the key point near the top lets a single passage be lifted without the rest of the page. - **Is it specific enough to quote?** A passage that answers the question directly, with sourced facts, is easier for a model to use than a vague one. Retrieval is also why the sources an engine cites shift from week to week. Our guide to [how AI answer engines source content](https://www.searchable.com/blog/ai-answer-engines-vs-traditional-search) covers that process in more detail. The only way to know whether these engines retrieve and cite your pages is to track their answers. [Searchable](https://www.searchable.com/features/aeo-insights/sources) records which URLs AI engines such as ChatGPT and Perplexity cite for your tracked prompts, so you can see which of your pages get used and which competitor pages get picked instead. For the metric itself, see [AI citations](https://www.searchable.com/glossary/ai-citations). ## Frequently asked questions ### What does RAG stand for in AI? RAG stands for retrieval-augmented generation. The system retrieves relevant documents, adds them to the model's prompt, and the model generates its answer from them. ### Is ChatGPT a RAG system? When it searches the web, yes. OpenAI describes ChatGPT search as turning your question into search queries and writing an answer from the results, with links to the sources. When ChatGPT answers without searching, it relies on its training data alone. ### What is the difference between an LLM and RAG? An LLM is the language model itself, and it answers from what it learned in training. RAG is a system built around an LLM that hands it relevant documents at answer time, so the model writes from those sources as well as its training. ### Does RAG stop AI hallucinations? It reduces them but doesn't stop them. The model can still misread a source, blend two sources together, or fill a gap with something invented, and if the retrieved page is wrong, the answer usually is too. ### What is agentic RAG? Agentic RAG lets the model plan its own searches instead of running one fixed retrieval step. It can split a question into several searches, check whether the results are good enough, and search again before it answers, much like AI search engines fanning one question out into related queries. ### Is RAG outdated? No. Newer models can read far more text at once, and some systems now plan several searches instead of one, which is the idea behind agentic RAG. Retrieval is still how AI search engines bring current web pages into an answer, because no model can fit the whole web into its prompt. ### What is GraphRAG? GraphRAG is a version of RAG developed by Microsoft Research. It first uses a language model to build a knowledge graph of the entities in a document collection and how they relate, then retrieves from that graph. It's designed for questions that need connections across many documents, such as summarizing the main themes in a large dataset. ### Why does RAG matter for SEO and AI visibility? Perplexity and Google AI Overviews pull web pages before they answer, and so does ChatGPT when it searches. A page that isn't retrieved can't be cited, so content with clear, self-contained passages that answer the question directly has a better chance of being used. ## Related terms - [Grounding](https://www.searchable.com/glossary/grounding): Grounding in AI means tying a language model's answer to specific source material, such as live search results or a company's own documents, so the model isn't relying only on what it learned in training. A grounded answer can point to its sources, which is why AI search engines show citations. - [Embeddings](https://www.searchable.com/glossary/embeddings): Embeddings are lists of numbers that represent the meaning of a piece of content, such as a paragraph of text or an image. Content with similar meaning gets similar numbers, so a system can find a relevant passage even when it shares no words with the question. Embeddings are what make semantic search and AI retrieval work. - [Content chunking](https://www.searchable.com/glossary/content-chunking): Content chunking is the way AI search systems split a web page into smaller passages so each one can be retrieved and quoted on its own. The term also describes writing pages in clear, self-contained sections, so any passage an engine pulls out still makes sense without the rest of the page. - [Semantic search](https://www.searchable.com/glossary/semantic-search): Semantic search is a way of finding information by matching the meaning of a query instead of its exact words. It lets a search engine or AI assistant return a page about "affordable CRM for startups" when someone asks for a "cheap customer database for a new company," even though the two phrases share almost no words. - [AI citations](https://www.searchable.com/glossary/ai-citations): AI citations are the links an AI engine attaches to its answer to show which web pages the information came from. Each citation credits a specific URL. A brand mention is different: the engine names a company in the text, even when it doesn't link to that company's site. - **AI hallucination**: A confident but false statement from an AI model, such as a price your product never had or a source that doesn't exist. --- [AI Search Glossary](https://www.searchable.com/glossary) | [Searchable Homepage](https://www.searchable.com)