GEO Explained: How to Optimize for Generative Engines

In short: GEO (Generative Engine Optimization) positions your brand in the syntheses, comparisons and guides AIs generate, by acting on the sources and consensus they have internalized.

Many confuse AEO and GEO. Yet the nuance is decisive. AEO targets the direct answer to a question. GEO shapes how a generative model describes an entire market: which players it mentions, in what order, with what attributes.

How an LLM builds an answer

Unlike a classic engine, a language model does not consult the web in real time for every query. It internalized vast corpora during training, then sometimes completes with live retrieval. Its answer is a probabilistic reconstruction of what it most often saw associated with your topic.

The implication: to be cited, a nice website is not enough. You must be present, consistently, in the sources that make up that corpus.

The sources models prioritize

• Reference publications and specialized press.
• Encyclopedias and structured knowledge bases.
• Expert forums and communities (Reddit, sector Q&A).
• Review platforms and verifiable third-party data.

The more convergently your brand appears across these spaces, the more the model treats it as a legitimate reference.

The role of consensus

A model does not “believe” a single source: it weights repetition and agreement between independent sources. This is the principle of source consensus. A brand that ten credible sources describe the same way will be cited far more reliably than one making the claim alone on its own site.

This is why serious GEO relies on authentic PR and genuine expert contributions — never fake reviews or astroturfing, which are eventually detected and penalized.

Building semantic authority

The ultimate goal of GEO is semantic authority: the fact that, for a given category, your brand is strongly associated with the right concepts in the model’s statistical mind. The Hills Agency orchestrates this presence systematically, source by source.

Takeaway: you do not “hack” an LLM. You earn its citation by becoming, in the eyes of its sources, the obvious reference of a category.