Retrieval-Augmented Generation
A technique that improves AI answers by retrieving relevant external information and feeding it to the model before it responds.
In plain English
Retrieval-augmented generation lets an AI look up relevant facts before answering. Instead of relying only on memory, it pulls in the right documents and uses them to give a more accurate reply.
Technical definition
Retrieval-augmented generation is an architecture that combines a retrieval component with a generative model. A query is embedded and matched against an external knowledge base, and the top results are inserted into the model's context so generation is grounded in retrieved evidence rather than parametric memory alone.
Business use case
Businesses use RAG to build assistants that answer questions from their own knowledge bases, policies, and product docs without retraining a model. This keeps answers accurate and current while protecting proprietary data and reducing hallucinations.
Example
An internal HR chatbot uses RAG to retrieve the company's latest leave policy document and answers an employee's question using that exact text.
Frequently asked questions
Retrieval-augmented generation, or RAG, is a method that fetches relevant documents from a knowledge source and provides them to a language model so its answer is grounded in accurate, up-to-date information.
RAG lets a model answer using current, private, or domain-specific data it was never trained on, which reduces hallucinations and keeps responses accurate without retraining the model.
A user query is converted into an embedding, the most relevant documents are retrieved from a vector store, and those documents are added to the prompt so the model can generate a grounded response.
Keep exploring
Embeddings
Embeddings turn words, sentences, or images into lists of numbers that capture their meaning. Things with similar meaning get similar numbers, so a computer can tell what is related.
Large Language Model
A large language model is an AI trained on huge amounts of text so it can read your question and write a useful answer. It powers chatbots and writing assistants.
Hallucination
An AI hallucination is when a chatbot confidently says something that is simply not true. It can invent facts, names, or sources that look real but are made up.
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