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AI architecture that retrieves relevant information from external sources in real-time before generating responses.
RAG (Retrieval Augmented Generation) is a technical approach where AI systems first search and retrieve relevant information from external sources (web pages, databases, documents) before generating a response. Unlike training-based models that rely solely on static training data, RAG systems access current information dynamically. Platforms like Perplexity, ChatGPT Search, and Google AI Overviews use RAG to provide up-to-date answers with source citations. For GEO, RAG systems are critical because they can discover and cite your content in real-time, making content freshness and crawler accessibility more important than ever.
Qwairy helps you optimize for RAG-based AI systems by tracking real-time citations, monitoring crawler access, and analyzing which content gets retrieved most frequently. Our platform identifies RAG citation opportunities and measures your performance across RAG-powered platforms like Perplexity and ChatGPT Search.
Automated web client operated by an AI company for model training, AI search, or user-requested retrieval.
Reference to a URL or website as a source of information in an AI response.
The process of anchoring AI responses in verified, real-world data sources to ensure factual accuracy.
When an AI model generates factually incorrect, fabricated, or misleading information presented as truth.
Recency and regular update frequency of content, signaling current relevance to AI systems.
Source a visitor arrives from, indicated by the HTTP referer header; AI referrers like chatgpt.com reveal traffic earned from AI citations.
Text file placed at the root of a website to indicate to indexing robots which pages to explore or avoid.