# What is generative engine optimization? Canonical: https://llmcitely.com/guides/what-is-generative-engine-optimization Publisher: LLM Citely Reviewed: 2026-10-30 Generative engine optimization is the work of improving how a business is represented in AI-generated answers. The useful starting point is a buyer's question and the evidence behind the answer. ![GEO is an evidence loop, not a new score](https://llmcitely.com/images/editorial/geo-evidence-loop.svg) The loop connects an observed answer to a specific change. It does not promise that the change will cause a citation. ## Start with a decision someone has to make Imagine an operations manager choosing analytics software for a small agency. “Analytics software” names a category; “Which analytics tools let an agency explain results to several clients?” describes a decision. The second question tells you who needs help, what matters and which product facts an answer would need. This is an illustrative situation, not a collected customer conversation. Write your own version using a question from a sales call, a support exchange or a client brief. Put the audience and the requirement in the question; leave out your brand when the task is discovering options. A separate question that names the brand can check whether its facts are represented correctly. The output of this step is a short, approved question set. A long list of generated variations is less useful if nobody can explain which buying decision each one represents. [Choose a buyer-question baseline](https://llmcitely.com/guides/choose-buyer-prompts) before collecting answers. ## Read an answer as several pieces of evidence An answer can name a business, describe one of its capabilities and attach a source link. Those are related observations, but they tell you different things. Consider this invented answer fragment: “Northstar is worth evaluating for client reporting,” followed by a link to a third-party comparison. The answer contains a Northstar mention. Its visible citation belongs to the comparison page. It does not show that Northstar's own website was cited, and it does not tell you whether the comparison actually supports the claim until you open it. For every answer you investigate, keep the question, answer text, visible source URL and collection context together. Highlight the sentence under discussion, then open the source to find the relevant passage. A gap between the sentence and the page is a concrete reason for review. A bare domain-count chart cannot show that relationship. [Mentions and citations](https://llmcitely.com/guides/citations-vs-brand-mentions) have separate jobs in the analysis. ## Follow one question through the work A small GEO cycle has a beginning and an end. Choose an approved question, obtain an actual answer through a clearly identified experience, and inspect the source material. Decide whether the useful next action is on your own page, a documented access rule or an inaccurate external description. Suppose a fictional product's answer page never explains whether reports include the underlying sources. The action is to establish the real export behavior with the product owner and publish an accurate explanation. “Improve GEO” is too broad to finish; “Explain which evidence appears in an exported report, with an example a reviewer can inspect” has an acceptance test. After the page work is complete, repeat the compatible question and compare the evidence. Keep two outcomes in the report: the source page was corrected, and the observed answer either changed or did not. Both are useful results. [The measurement guide](https://llmcitely.com/guides/how-to-measure-ai-visibility) shows how to make the sample behind that comparison visible. ![Trace the claim back to the source](https://llmcitely.com/images/editorial/geo-source-trail.svg) Trace one buyer question through the saved answer, explicit source URL and action your team owns. Each row shows what that record can establish before the next recheck. ## The website still has a job A useful product page helps a person verify the same facts an answer might summarize. State what the product does, who it serves, what inputs it accepts and what output a user receives. Put limitations next to the capability they qualify, rather than hiding them in a broad disclaimer. For an agency-reporting page, a reader should be able to find a sample report, understand its fields and learn how the evidence connects to the summary. A description such as “powerful insights for every team” leaves the task unfinished. Google's [AI-feature documentation](https://developers.google.com/search/docs/appearance/ai-features) says its AI search experiences use the existing Search eligibility foundation. For those experiences, adding a special text file or AI-specific schema is not a substitute for a useful, accessible page. [Audit a page](https://llmcitely.com/guides/run-a-geo-audit) when you are unsure whether its basic job is being completed. ## Make the first deliverable small enough to inspect A first project can contain one audience, one market, a reviewed question set and an evidence-backed action list. Agree which AI experience is being observed and which questions belong in the baseline. Label generated suggestions as suggestions and missing observations as missing. Your handover should let another person trace a summary to a question and an original answer. It should also show which work is complete and which questions still need investigation. That is more informative than a single score whose population and formula are unclear. Use [GEO vs SEO](https://llmcitely.com/guides/geo-vs-seo) to explain the shared website work, or open the [workflow demonstration](https://llmcitely.com/demo) to see how questions, evidence and delivery fit together. ## Questions, answered ### Is GEO a replacement for SEO? GEO adds an answer-focused measurement and improvement workflow. Discoverable, useful web pages remain important; many access, content and internal-link tasks serve both. ### Can GEO guarantee a brand appears in an answer? No. The observed engine, question wording, market and time all affect the sample. Report evidence and scope rather than promising a fixed placement. ### Where does LLMO fit? In marketing, LLM optimization is often used for work overlapping GEO. The term also describes model engineering, so define the marketing task explicitly rather than treating every LLMO query as relevant. ## Sources and further reading - [AI features and your website](https://developers.google.com/search/docs/appearance/ai-features) - [Creating helpful, reliable, people-first content](https://developers.google.com/search/docs/fundamentals/creating-helpful-content) Examples marked illustrative are not customer measurements. ## Continue with the next step - [Build a measurement plan](https://llmcitely.com/guides/how-to-measure-ai-visibility) - [Run a scoped GEO audit](https://llmcitely.com/guides/run-a-geo-audit) - [Explore the workflow](https://llmcitely.com/demo) - [aeo vs seo](https://llmcitely.com/guides/aeo-vs-seo) - [geo vs aeo](https://llmcitely.com/guides/geo-vs-aeo) - [Compare GEO with SEO](https://llmcitely.com/guides/geo-vs-seo) - [Understand answer engine optimization](https://llmcitely.com/guides/what-is-answer-engine-optimization) - [ai search optimization platform](https://llmcitely.com/product) ## Your next step [Explore the workflow](https://llmcitely.com/demo)