GEO shares DNA with SEO – both strive to make your content more discoverable – but there are important differences in tactics and focus:
- Similarities: GEO and SEO both emphasize understanding user intent, using relevant keywords (or questions), providing quality content, and ensuring crawlability. Core SEO best practices like fast loading, mobile-friendly pages, and logical site structure remain important; generative engines still rely on the web content they can fetch and read, so technical SEO issues can prevent your content from being seen by AI. Additionally, E-E-A-T principles (Experience, Expertise, Authoritativeness, Trustworthiness) are arguably even more important in AI contexts. Both SEO and GEO reward authoritative content and penalize (or ignore) low-quality, misleading content. Data and analytics are used in both to iterate strategy – albeit the data sources expand in GEO (as discussed in Step 0).
- Differences in Result Format: The biggest difference is how success looks on the SERP. In SEO, success is ranking #1 and getting the click. In GEO, success might be being mentioned within an AI answer (with or without a click). Your content could be blended with others in a single answer. For instance, an AI answer to “What’s the best SUV for families?” might say: “According to CarSite A, the XYZ SUV ranks top for safety, and Expert B from AutoBlog notes its spacious interior…” – combining sources. So you’re competing not just to rank, but to be included in a synthesized answer.
- New Optimization Targets: With traditional SEO, you optimize for algorithms that rank whole pages. With GEO, you may need to optimize for passage-level utility and contextual relevance. Generative models might pull just one paragraph from your 2,000-word article. That means that specific paragraph needs to stand well on its own (answering a sub-question clearly) to be chosen. Featured snippet optimization experience helps here, but AI may go further by pulling multiple snippets and merging. Moreover, models may prefer certain writing styles – e.g., clear, declarative sentences that are easy to parse and rephrase. We’ll cover content guidelines in Step 4.
- Retrieval Mechanisms: SEO is about ranking in a search index. GEO is about being retrieved and then integrated. Ensuring your content is chosen by the retrieval step (which uses Semantic Search) is critical. Semantic or vector search means the engine looks for meaning, not exact keyword matches. So old-school tricks like exact-match keywords are less relevant; instead, thorough coverage of a topic and natural language may matter more. In GEO, entities and semantics are king – the AI might associate concepts rather than literal keywords. We’ll address optimizing for RAG (Retrieval-Augmented Generation) in Step 5.
- Competitive Landscape: In SEO, you might look at competitors on the SERP. In GEO, the “competitors” might be any content on the web that the AI finds relevant, even if they weren’t top of SERP. Indeed, a study found SGE sometimes pulls sources from beyond the top 50 rankings, not just the first page. This broadens the competitive field. It also means GEO can level the playing field: if you have the most relevant info on a subtopic, the AI could surface you even if your domain’s SEO strength is lower. On the flip side, high-ranking sites can no longer assume they will get the click if an AI answer satisfies the user with info from various sources, including perhaps that high-ranking site’s content summarized.
In short, SEO is necessary but not sufficient for GEO. As one definition puts it: “GEO is optimizing your website’s content to boost its visibility in AI-driven search engines such as ChatGPT, Perplexity, Gemini, Copilot and Google AI Overviews”. You still need solid SEO foundations (you won’t appear in AI results if your site isn’t indexed or trusted), but you also need to go beyond – thinking about how AI picks, combines, and attributes information.