What is Generative Engine Optimization (GEO) and how does it work?
Generative Engine Optimization is the practice of improving how a business’s information can be found, understood, and used in AI-generated answers. In practice, it combines accessible pages, useful evidence, clear explanations, and observation of citations and business outcomes rather than relying on a guaranteed AI ranking formula.
What is being optimized?
There are several possible outcomes: accurate company descriptions, inclusion in a relevant shortlist, a source link to your website, and a visit that becomes a qualified subscriber. Treat them as separate measures. A general educational article may be cited without recommending the author’s business. A recommendation may draw on another website. Specify which outcome a project is trying to improve before deciding what to publish.
Where does the term come from?
The paper “GEO: Generative Engine Optimization” by Aggarwal and colleagues investigated ways to improve source visibility in generative answers. Its experimental findings are not a service-level promise for current commercial search products. Use research to form testable ideas, then verify current provider guidance and your own observations. Adding a statistic to every paragraph without regard to relevance misunderstands the purpose of evidence.
What does a practical GEO workflow look like?
Start with buyer questions and inspect existing coverage. Fix access problems, then create or improve the best page for each intent. Give the answer early, explain the conditions, and provide evidence that can be checked. Add a relevant next step, such as an assessment. Finally, repeat a documented prompt panel and measure actual visitors and subscribers. This is a cycle of publishing and evaluation, not a one-time schema installation.
How does this apply to an AI consultancy?
A useful page about automated proposal follow-up might show where an enquiry enters, who approves the message, how the CRM records it, and what happens when information is missing. A worked example gives the reader something to evaluate. The same page can then invite an assessment of their workflow. A readiness tool and a citation tracker serve different roles: one checks conditions, while the other observes answers produced elsewhere.
What should you put on your working checklist?
| Stage | Question to answer |
|---|---|
| Find | Can the relevant public page be retrieved? |
| Understand | Does it clearly state the service and audience? |
| Verify | Can a reader inspect the supporting evidence? |
| Act | Is the next step appropriate and clear? |
| Measure | Did useful citations, visits, or subscribers result? |
Which related questions should you explore?
Who is behind this guidance?
Which sources can you check?
What else should you know?
Is GEO a replacement for SEO?
No. The practices overlap, and technical discovery remains important.
Does GEO guarantee AI citations?
No. Source selection varies with the question, system, and context.
Is GEO only about content formatting?
No. Evidence, access, business clarity, and measurement are also part of the work.
Exploring AI too?
Have an interesting experience or a question? Feel free to get in touch. I am always happy to exchange perspectives on practical uses of AI.
hello@mikloskovacs.io
