RAG (Retrieval-Augmented Generation)
RAG is an AI architecture in which a model searches for data from external sources before responding. Learn how it works and why it’s important for GEO.
RAG, generation with search An architecture in which an LLM queries an external knowledge base before responding and receives relevant data in a context box. The model does not conjecture, but relies on a specific source.
In GEO-promotion, RAG directly determines whose content falls in response to AI. If the page is structured for fragmentary extraction – chunky, clear headings, specific facts – the chances of being in the context of the model are higher. Sites without these features simply do not participate in the sample.
Related terms
Fine-tuning (model training)
Fine-tuning – additional LLM training on highly specialized data. What is it, what is different from RAG and what approach is more important for GEO promotion.
Artificial intelligencellm
LLM (Large Language Model) is a neural network behind ChatGPT, Claude and Gemini. How it works, why it’s wrong and what it means for SEO and content marketing.
Artificial intelligenceLLM hallucination
LLM hallucination is when an AI generates compelling but false text. What is it, why does it arise, and how does it affect SEO and AI search?
Artificial intelligenceContext Window (Context Window)
Context window is the volume of text that a language model sees at a time. How the token limit works and why dense content is more likely to fall into AI search responses.
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