RAG (Retrieval-Augmented Generation)

RAG (Retrieval-Augmented Generation) is an LLM technique where the model first retrieves relevant facts from an index or knowledge base, then generates an answer based on them.

• •Unlike "pure" LLM calls (where the model answers only from its parameters), RAG allows:
• •Access to current information (news, prices, documents)
• •Reduced hallucinations — answers are grounded in specific sources
• •Source citation — users can verify facts

Where RAG is used in search

• •RAG powers all modern AI search engines:
• •Perplexity — RAG as the core mechanism
• •ChatGPT Search — RAG over the Bing index
• •Google AI Overview — RAG over Google's own index
• •Bing Copilot — RAG in the Microsoft stack

All of them follow: user query → retrieve top-N documents → pass documents to LLM with instructions like "answer using these sources".

What it means for SEO

Since RAG systems cite specific sources, your content must be "extractable": structured, with direct answers in opening paragraphs, with proper schema markup. This is the essence of GEO optimization.