Quick answer: Modern AI search runs on RAG – Retrieval-Augmented Generation. Instead of answering purely from training memory, the engine retrieves relevant live documents, then generates an answer grounded in them, with citations. Google’s SGE (now AI Overviews), Perplexity and ChatGPT search all follow this pattern – and understanding it tells you precisely where you can influence the outcome.
From SGE experiment to AI Overviews
Google’s Search Generative Experience launched as a lab experiment and matured into AI Overviews rolled out worldwide. The architecture stayed constant: Google’s index supplies candidate documents, ranking systems shortlist them, and a Gemini model synthesises a cited answer. The generative layer changed the interface; the underlying selection remained recognisably search.
How RAG works, step by step
1. Query understanding. Your question is expanded and interpreted – often into multiple sub-queries.
2. Retrieval. The system fetches candidate passages from an index (Google’s, Bing’s, or the engine’s own crawl).
3. Ranking and filtering. Candidates are scored for relevance, authority, freshness and consistency with each other.
4. Generation. The LLM writes an answer constrained to the retrieved material, attaching citations.
Where you can influence each stage
Stage 2 is won by indexation and crawler access (crawlability guide). Stage 3 is won by authority, freshness and entity clarity. Stage 4 is won by extractability – answer-first passages, statistics and tables the model can lift cleanly. This is why GEO advice converges on the same fundamentals: each maps to a RAG stage.
Why RAG matters more than model quality
Engines swap models constantly, but retrieval pipelines persist. A site optimised for retrieval and extraction stays visible across model upgrades – which is also why tracking should be continuous. IndexGraph.ai monitors your citations across all four major RAG-based engines as they evolve.
FAQ
Do all AI answers use RAG?
No – evergreen questions are often answered from training memory alone. That split is why GEO targets both live retrieval and long-term entity building (ChatGPT guide covers both tracks).
Adexorb Technologies optimises every stage of the RAG pipeline so AI engines retrieve, trust and cite your pages.