core
RAG (Retrieval-Augmented Generation)
The architecture every AI answer engine uses — retrieve documents, ground the generated answer in them.
Definition
Retrieval-Augmented Generation (RAG) is the architectural pattern where a language model is augmented with a retrieval step. Given a user prompt, the system first fetches relevant documents (via embeddings, BM25, or hybrid retrieval), then synthesizes an answer grounded in those documents with inline citations. Every modern answer engine — Perplexity, ChatGPT search, Claude web tool, AI Overviews — uses RAG.
Example
When you ask Perplexity 'what is RAG', it retrieves ~5 web pages, then writes a paragraph synthesizing them with inline [1][2][3] citations.How to optimize
Optimize for the retrieval step, not the synthesis step. Your goal: be in the top-5 retrieval cohort. Use BLUF format, high E-E-A-T, schema.org markup, and semantic alignment to buyer queries.