Retrieval-Augmented Generation (RAG)
AI technique that enhances LLM responses by retrieving relevant information from external databases before generating answers.
Definition
Retrieval-Augmented Generation (RAG) is an AI technique that combines information retrieval with text generation. Before generating a response, RAG systems first search a knowledge base or document collection to find relevant information, then use that retrieved context to generate more accurate, up-to-date answers.
RAG helps solve key LLM limitations like outdated training data and hallucinations. By grounding responses in retrieved sources, RAG systems can provide more factual, verifiable information with source attribution.
Why It Matters
RAG is the foundation of most AI search engines like Perplexity. Understanding RAG helps marketers grasp why content structure, factual accuracy, and clear sourcing matter for GEO—these factors determine whether content gets retrieved and cited.
As more tools use RAG architectures, content that's easy to retrieve and verify will have a competitive advantage in AI-powered discovery.
Examples in Practice
Perplexity uses RAG to answer questions: it first searches the web for relevant pages, retrieves key information, then generates a comprehensive answer with inline citations.
A company builds an internal chatbot using RAG that searches their documentation and knowledge base to provide accurate, source-backed answers to employee questions.