What Is Retrieval-Augmented Generation?
Retrieval-augmented generation (RAG) is a technique that pairs an AI model with a retrieval step — searching a knowledge source in real time — before generating a response, rather than relying solely on what the model memorised during training.
How It Works
When a user asks a question, a RAG system first retrieves relevant documents or passages from a defined source (the web, a specific database, a company’s own content), then passes that retrieved material to the LLM as additional context alongside the original question. The model’s answer is generated using that fresh, specific information rather than purely its training data.
Why It Matters for GEO
Most AI search tools — Perplexity, Google AI Overviews, Bing Copilot — use some form of RAG to ground their answers in live web content. This is precisely why being retrievable matters: clear, well-structured, up-to-date, and easily extractable content is more likely to be pulled into a RAG system’s retrieval step in the first place, which is a prerequisite for being cited at all.
