Generative Engine Optimization is about making your content the source AI models cite when users ask questions in your niche. Learn how LLMs retrieve content, how to build entity authority, and how to measure your AI presence.
GEO is not a replacement for SEO - it is a new layer that requires different signals, different metrics, and a different mental model. This article explains the core distinctions, introduces the four GEO readiness signals, and gives you a framework for deciding where to start.
AI answer systems do not rank URLs - they retrieve text passages. Understanding the RAG (Retrieval-Augmented Generation) architecture tells you exactly why some pages get cited and others do not, even when both rank well in traditional search.
AI RAG systems retrieve text at the passage level, not the page level. The way you write and structure each paragraph determines whether your content is extracted as a citation. This guide covers the specific writing and formatting moves that improve passage-level extractability.
AI knowledge graphs represent your brand as an entity with attributes. The more consistently that entity is defined across your own site and external authoritative profiles, the higher the confidence AI systems assign to your content as a citation source. This guide covers Organization schema, sameAs links, and the entity consistency audit.
AI citation authority is built from the same raw material as traditional SEO authority - external mentions and links - but weighted differently. Editorial context, topical co-citation, and original research citations matter more than link volume. This guide covers the off-page strategy that improves AI citation rates.
Measuring GEO performance requires different tools and metrics than traditional SEO. This audit guide covers the four-platform citation test, the citation frequency baseline, GA4 AI referral traffic tracking, and the quarterly audit cadence that makes GEO improvement visible over time.