Generative engine optimization (GEO)
The practice of improving a brand's presence in generative AI answers.
What is Generative engine optimization (GEO)?
GEO combines technical accessibility, entity clarity, authoritative coverage, third-party corroboration, and measurement across AI models. It is not a single markup change. The work begins by observing the prompts and sources that shape answers, then improving the evidence available to the systems producing them.
A security vendor finds that AI answers cite three industry roundups, earns inclusion in two, and publishes a benchmark study that becomes a new source.
Why it matters for AI visibility
AI answers can shape awareness and buying decisions before a person visits a website. Understanding generative engine optimization (geo) helps teams connect what a model says with the prompts, sources, and technical signals that influenced the result. The useful next step is to observe the evidence, compare it over time, and act on the specific gap.
How to measure or use it
- Define the scope. Choose the relevant market, prompts, models, locations, and time window.
- Keep the evidence. Save the complete answer, its citations, and the page or claim being evaluated.
- Compare patterns. Look for repeated movement across prompts and models instead of reacting to one response.
- Take a specific action. Improve the owned page, correct a claim, earn third-party inclusion, or create better evidence.