RAG (Retrieval Augmented Generation)

RAG (Retrieval Augmented Generation) is a technique that combines information retrieval with AI text generation. AI models don't remember everything: they have Knowledge Cut-Off and limited context windows, so RAG supplies them with context on the fly. The RAG pipeline consists of 6 steps: preparing a knowledge base, chunking documents, generating embeddings, storing them in a vector database, retrieving the most relevant fragments, and generating an answer.

The key principle is that less context = better answer; 3 ideal fragments give better results than 50 random pages. RAG is the foundation of AI Overviews in Google, ChatGPT Search, Perplexity, and every enterprise chatbot. For SEO, this means content must be 'citable': autonomous chunks (H2 sections), BLUF at the start of sections, and high EAV saturation.

Analogously, RAG is like a lawyer's assistant — the lawyer (LLM) is smart, but the assistant (RAG) brings him the 3 most important files from the archive.