Compare the questions, not the acronyms
| Dimension | AEO emphasis | GEO emphasis |
|---|---|---|
| Starting question | Can this page answer the reader’s question clearly? | How does a generative response use and portray the information? |
| Typical review | Answer, qualification, source and page accessibility. | Complete response, cited URLs, factual accuracy and context. |
| Useful artifact | A question-to-page map with claim-level sources. | A dated response record with explicit scoring rules. |
| Not sufficient | Adding an FAQ or definition box. | Counting mentions without examining their meaning. |
These distinctions organize this wiki; they are not platform-mandated categories. The GEO paper supplies a research definition of generative-response visibility, while Google’s guidance keeps the technical foundation within existing SEO. GEO paper; Google guidance.
The shared foundation
A page cannot become trustworthy merely by being easy to extract. It needs a useful answer, evidence for material claims and enough context to avoid misleading a reader. Conversely, a well-supported answer is hard to use when its important content is inaccessible.
Google lists crawl access, textual content, internal links and visible-content-matching structured data among relevant fundamentals for its AI features. It does not prescribe separate AEO and GEO implementations. Source: Google.
Example: one page, two reviews
Illustrative example. A software guide explains whether a data export preserves timestamps. An answer-readiness review checks whether the answer names the export format, timezone behavior and relevant product version. A generative-response review checks whether an assistant cites that guide and retains those qualifications.
Those reviews can lead to a single well-maintained page. Creating a near-duplicate “GEO version” would not resolve a factual omission and could leave readers unsure which policy is current.
Choose a useful next step
If readers cannot find a clear answer, start with answer engine optimization. If you need to interpret generated answers, start with generative engine optimization and establish a recording method first.
What this comparison does not prove: that either label predicts ranking, that an engine follows a fixed sequence, or that a consulting engagement is necessary. The practical unit of work is a reader question, a page and an inspectable result.