Cluster 2 of 9

2. How AI works

You don’t need to be an engineer to do GEO well. But you do need to understand how AI systems retrieve, weigh and process information into an answer. That mechanism — largely ordinary search technology, no mystery — helps determine who ends up in the answer.

This cluster explains how large language models work, the difference between training data and live retrieval, how RAG functions, and which signals determine whether a source is cited or ignored. Technical enough to understand, practical enough to act on.

→ Start with ‘How LLMs retrieve information‘ for the big picture, or jump straight to ‘RAG explained‘ for the most actionable insights.

In this section

2.1

How LLMs retrieve information

What determines which content an AI model retrieves and uses? A clear explanation of the two sources of AI knowledge and the GEO implications for marketers.

2.2

RAG explained

RAG — Retrieval Augmented Generation — determines whether AI systems can retrieve your content at the moment of the question. One widely used setup explained step by step, as a simplified example.

2.3

Training data vs live retrieval

AI models operate on training data and live retrieval simultaneously. What does that mean for your content strategy and how do you address both layers?

2.4

Citations and source logic

Relevance, authority, structure, uniqueness and currency — five factors that keep returning in how AI systems choose sources. With the honest caveat: nobody knows the weighting, and it differs per channel.