Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) is the architecture behind every AI search engine that cites sources. When ChatGPT, Perplexity, or Google AI Overviews answers a question, it doesn't just generate from memory — it retrieves relevant documents, pastes them into its context window as evidence, then generates a response grounded in that retrieved material. RAG is the operating system of AI search.

Why RAG Matters for Brands

RAG changes the economics of visibility fundamentally. In traditional search, you optimize to rank on a results page. In RAG-powered AI search, you optimize to be retrieved into the context window. If your content isn't in the retrieval set, the AI has no evidence to cite you — and you don't exist in the answer.

This is not a marketing trend. It's a brand-risk function. 80% of the pages ChatGPT cites don't rank in Google's top 100 results, which means the retrieval layer is operating on entirely different criteria than traditional search rankings. Brands optimizing exclusively for Google are optimizing for a system that RAG doesn't use.

How RAG Works

The RAG pipeline has three stages:

1. Retrieve. The system queries an index of documents — web pages, knowledge bases, publication archives — and pulls the most relevant chunks into the context window. This is where entity signals and entity optimization determine whether your brand's content gets selected.
2. Augment. The retrieved documents are injected into the LLM's prompt as evidence. The model now has both its parametric knowledge and fresh, source-grounded material to work with.
3. Generate. The LLM synthesizes an answer from the retrieved evidence, attributing claims to specific sources. This is where citation architecture determines whether your content gets cited or just consumed.

RAG as Brand Risk

The retrieval set is a zero-sum game. AI engines retrieve a finite number of documents per query — typically 5-20 sources. If a competitor's content is more structured, more authoritative, and more extractable than yours, they occupy slots you don't. Every query where you're absent from the retrieval set is a query where the AI literally cannot recommend you, regardless of your actual product quality or market position.

Generative Engine Optimization is, at its core, the practice of earning your way into RAG retrieval sets at scale.