Retrieval-augmented generation (RAG) is a technique used by AI systems that combines a language model’s trained knowledge with real-time retrieval of information from external sources. Rather than relying solely on what was in the training data, a RAG-enabled system searches a knowledge base or the live web at query time and uses the retrieved content to inform its response.
Perplexity operates on a RAG architecture, retrieving current web pages before generating its answer and citing those sources in the response. Google AI Overviews use a similar approach. This is why content that is currently indexed and accessible can appear in AI-generated answers even if it postdates the model’s training cutoff.
For GEO purposes, RAG-enabled systems are more responsive to recent content than pure LLMs. A well-structured page published today can be retrieved and cited in a Perplexity answer within days of indexation. This makes technical SEO prerequisites, correct indexation, fast load times, clean URL structure, and structured data, directly relevant to AI answer visibility. A page that Google cannot crawl efficiently is also a page that RAG-based systems cannot retrieve and cite.
