Retrieval-Augmented Generation (RAG) is an AI technique that enhances large language model responses by retrieving and incorporating relevant information from external data sources in real-time.
RAG is important for AI visibility because it determines which sources AI systems cite:
How RAG works: 1. User submits a query to an AI system 2. The system searches a knowledge base or the web 3. Relevant documents are retrieved and ranked 4. Retrieved information is fed to the LLM with the query 5. LLM generates a response informed by retrieved content
For brands, RAG means:
- Having recent, accurate content matters (it can be retrieved)
- Technical SEO affects whether your content is retrieved
- Content structure helps RAG systems extract relevant info
- Authority signals influence retrieval ranking
Perplexity, ChatGPT with web browsing, and Google AI Overviews all use forms of RAG. Optimizing for RAG means ensuring your content is discoverable, authoritative, and well-structured.