AI & Automation
Retrieval-Augmented Generation
Definition
Retrieval-augmented generation (RAG) is a technique that first retrieves relevant documents from a trusted source, then has an AI model generate an answer grounded in that retrieved content.
What is retrieval-augmented generation?
Retrieval-augmented generation (RAG) is a technique that combines two steps, retrieval and generation, so that an AI model answers from trusted source material rather than from memory alone. First the system retrieves the documents most relevant to a question; then it passes those documents to a large language model, which generates an answer based on them. (The abbreviation RAG is used interchangeably; you will also see the phrase written without a hyphen as "retrieval augmented generation".)
The approach exists to solve a specific problem. A language model on its own answers from patterns learned during training, which may be outdated, generic, or simply wrong for your particular case. RAG adds a step that injects the right, current information at the moment the question is asked.
How retrieval-augmented generation works
A typical RAG pipeline runs in three stages:
- Retrieve. The system searches a knowledge source, often using vector search over embeddings, to find the passages most relevant to the question.
- Augment. Those retrieved passages are added to the prompt given to the language model, as the context it should answer from.
- Generate. The model writes a natural-language answer grounded in that supplied content, rather than inventing one.
Because the knowledge lives in the source rather than the model, keeping answers up to date is a matter of updating the documents, not retraining anything. This is a practical form of AI grounding.
Why RAG reduces hallucination
The main benefit of RAG is accuracy. An AI hallucination happens when a model fills a gap with a plausible guess; RAG removes much of that gap by handing the model the actual answer to work from, then asking it to stay within that content. When the source does not contain the answer, a well-designed system can say so rather than improvise.
This is precisely how a knowledge-grounded support assistant stays reliable. It draws on the same principle as knowledge-centered service: capture what you know in a maintained knowledge base, and let every answer flow from it. Retrieving from your documented content, then generating a reply anchored to it, is what lets an AI answer customer questions accurately, in your own words, and defer to a person when the content runs out.
Why it matters
Example
A customer asks a support assistant how to reset their password. Instead of answering from memory, the system searches the company's help centre, retrieves the two most relevant articles, and passes them to the language model, which writes a step-by-step reply based only on those articles. The answer reflects the current, documented process, and can link back to the source.
How Resolve247 helps
RAG, working on your knowledge base
Resolve247's AIChatbot answers by retrieving from your knowledge base and generating a grounded reply, with an anti-hallucination guarantee, so customers get accurate answers drawn from your own content rather than invented ones.
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Related terms
Frequently asked questions
What is retrieval-augmented generation?
Retrieval-augmented generation, or RAG, is a technique where an AI system first retrieves relevant documents from a trusted source and then generates an answer grounded in them. This keeps responses tied to real, current content rather than the model's memory alone.
How does retrieval-augmented generation work?
RAG runs in three steps: it retrieves the passages most relevant to a question, adds them to the prompt as context, and has a language model generate an answer based on that content. Because the knowledge lives in the source, updating the documents updates the answers.
How does RAG reduce hallucination?
Hallucinations happen when a model fills a gap with a plausible guess. RAG narrows that gap by giving the model the actual source material to answer from and keeping it within that content, so answers stay anchored to trusted information.
Why is RAG useful for customer support?
It lets an AI assistant answer from your own help centre and documentation, so replies reflect your real, current policies rather than generic training data. Resolve247's AIChatbot uses this approach to answer accurately and hand over to a person when the content does not cover a question.