Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation is an AI architecture that combines a language model's generative capabilities with real-time information retrieval from external knowledge sources. Instead of relying solely on training data, RAG-enabled models search the web or specific databases to find relevant, up-to-date information before crafting their response.

RAG is critical for AI search optimization because it creates a pathway for brands to influence AI responses without waiting for model retraining. When Perplexity searches the web to answer a query, or when ChatGPT browses with Bing, they are using RAG — and the content they retrieve directly shapes their responses.

Optimizing for RAG involves ensuring your content ranks well in the search indices these models query, is structured for easy extraction, and contains the authoritative signals that cause retrieval systems to prioritize your content over competitors.