1. Prepare knowledge
Documents or records are collected, cleaned and divided into useful passages.
What Is RAG?
RAG is a technical approach that retrieves relevant external information and supplies it to a generative model before or while the model creates an answer.
RAG definition
Retrieval-Augmented Generation, or RAG,
is an AI architecture that retrieves relevant information from an external source and uses that information as context when generating a response.
The term was introduced in a 2020 research paper describing models that combine parametric memory—the knowledge stored in model weights—with non-parametric memory retrieved from an external index.
RAG is used because a language model’s internal knowledge may be incomplete, old or too general for the task. Retrieval can provide current documentation, company records, research papers, product information or other domain-specific material.
How RAG works
2. Index it
The passages are stored in a searchable system, often using embeddings, keywords or a hybrid method.
3. Retrieve context
A user question is used to locate passages likely to contain the needed information.
4. Generate an answer
The model receives the question and retrieved context, then creates a response based on both.
Some systems add reranking, query rewriting, citation generation, tool calls, filters, permissions and post-generation fact checks. “RAG” therefore describes a family of architectures rather than one fixed implementation.
Why organizations use RAG
- To answer from private company documents without retraining a model.
- To use fresher information than the model’s original training data.
- To ground answers in a limited, approved knowledge collection.
- To provide source links or citations when the application supports them.
- To apply access controls so users retrieve only information they are allowed to see.
- To update the knowledge base more quickly than retraining a model.
Google also describes retrieval-augmented generation as one of the techniques used in its generative search features, where relevant and current pages can be retrieved from the Search index to support a response.
RAG limitations
RAG does not automatically make an answer correct. The final response depends on the quality of the knowledge base, indexing, retrieval method, query, permissions, prompt and model.
Retrieval failure
The best passage may not be found, or an irrelevant passage may rank higher.
Source failure
The retrieved material may be wrong, outdated, incomplete or contradictory.
Generation failure
The model may misinterpret the retrieved text or add unsupported claims.
Security failure
Weak permissions, prompt injection or careless document handling can expose information or alter behaviour.
Good RAG systems therefore need evaluation, monitoring, source maintenance and clear boundaries.
RAG versus GEO and REG
| Term | Type | Main question | Who applies it? |
|---|---|---|---|
| RAG | Technical AI architecture | How can an application retrieve external knowledge and use it to generate an answer? | AI engineers, product teams and developers |
| GEO | Publishing and visibility strategy | How can content become more useful or visible in generated answers? | Publishers, marketers, organizations and site owners |
| REG | Entity and retrieval guidance framework | How can the correct entity be recognized and the correct information be retrieved? | Businesses, publishers, data stewards and visibility specialists |
REG can improve the public information that a RAG-enabled system might retrieve, but REG is not part of the RAG code. GEO can improve the quality or usefulness of source content, but it does not control the retrieval pipeline.
Practical business example
A company builds an internal support assistant using manuals, policies and service records.
- RAG: retrieves the relevant manual section and current policy before generating the answer.
- REG: ensures each product, location and service is named consistently so the correct records can be recognized and retrieved.
- AEO: improves the wording of frequently requested answers.
- SEO and GEO: matter if the company also publishes selected support information on the public web.
Key sources
These references support the established definitions and technical explanations on this page.
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks — The 2020 research paper that introduced the RAG formulation.
- Google Cloud: Retrieval-Augmented Generation — A current technical overview of RAG.
- Google’s guide to generative AI features in Search — How retrieval and grounding relate to Google’s generative search.