SEO vs GEO: What's the Difference and Why You Need Both
Two retrieval systems, two different jobs
SEO and GEO are often pitched as a fight. They aren't. They are two different retrieval systems built on top of partly overlapping data, and a page can serve both, badly serve one, or fail at both. The useful question isn't which one to pick. It's what each one rewards, and how to write something that satisfies both at once.
How Google retrieves
A traditional Google result is the output of a crawl, an index, and a ranker. Google's crawler walks the web, builds a forward and reverse index of pages keyed by terms, and then, for a given query, the ranker scores candidate pages on hundreds of signals. PageRank is still in there, but the modern ranker leans heavily on link graphs, on-page relevance, query intent matching, freshness, and the Page Experience signals (Core Web Vitals, HTTPS, mobile usability).
The output is a list of ten blue links and, increasingly, an AI Overview summary above them. The ranking is per-query, the unit of competition is the page, and the user clicks into one of the results to read it.
How an LLM-driven engine retrieves
ChatGPT search, Perplexity, and Google's AI Overviews work differently. The mechanic, broadly:
- ›The user asks a question in natural language.
- ›The system rewrites that into one or more search queries, often more specific than what the user typed.
- ›It fetches the top results for those queries from a real search backend (Bing for ChatGPT, a mix for Perplexity, Google's own index for AI Overviews).
- ›It feeds the fetched pages into an LLM with a prompt that says, in effect, "answer the user's question using these sources, and cite them."
- ›The LLM synthesises an answer and links the citations inline.
The user does not click ten results. They read the synthesis, and at most click one citation to verify a claim. The unit of competition is the citation, not the page rank.
A worked example
Imagine the query: "fastest hosting for a Next.js site."
On Google, the results that win are the ones with strong domain authority on the topic, an exact-intent match in the title and H1, useful content past the fold, and decent freshness. A Vercel marketing page ranks. A Reddit thread ranks. A long benchmark post on a developer's personal blog ranks if it has links pointing at it.
In ChatGPT, ask the same question. The model issues a search like fastest Next.js hosting 2025 benchmark, fetches around five results, and writes a paragraph that says something like "Vercel and Netlify are common choices, with Vercel optimised specifically for Next.js. Independent benchmarks (citing one) put Cloudflare Workers and Vercel within a few milliseconds of each other on cold starts." The cited source is whichever page the model judged most authoritative and most extractable. Often that's the benchmark blog post, not the Vercel marketing page, because the benchmark has named methodology, numbers, and a date.
Same query. Different winners.
What each one rewards
| Signal | Google ranking | LLM citation | |---|---|---| | Inbound links from authoritative domains | High | Indirect (via the search step) | | Exact-match title and H1 to the query | High | Medium | | Direct, extractable answer near the top | Medium | High | | Named methodology, dates, named sources | Medium | High | | Page Experience (CWV, mobile) | High | Low | | Brand mentions across the open web | Medium | High | | Freshness | Medium | High for time-sensitive queries |
The overlap is real but uneven. A page that ranks well for an informational query usually also gets fetched in the LLM's retrieval step. Whether it gets cited depends on whether the LLM can extract a clean answer from it.
What this means for how you write
Most of the work that helps both is the same work. Some of it is specific to one or the other.
Both want a clear answer near the top. Lead with the answer. Put the nuance and the context underneath. A 2,000-word article that buries the answer in section seven is fine for SEO if the rest of the signals are strong; it's poor for citation because the LLM may not read that far before deciding what to extract.
Both want named, datable evidence. "Studies show" is filler. "Akamai's 2017 retail study found a 7 percent conversion drop per 100 milliseconds of delay" is a sentence the LLM can lift verbatim and cite, and a sentence Google understands as substantive content rather than padding.
Google wants link equity. The LLM wants quoted authority. Both come from the same place: writing something that other people in the field link to and quote. There's no shortcut here, and there's no plugin for it.
Schema helps Google more than it helps LLMs. FAQPage and HowTo markup still shape how Google displays your result. LLMs largely read the rendered text. Mark up your content properly, but don't expect schema alone to get you cited.
Brand mentions matter for LLMs in a way they didn't for classical SEO. When a model decides whose claim to trust, having seen your brand name across podcasts, GitHub readmes, and community posts moves the needle. This is slow work and it shows up months after you do it.
The summary
SEO and GEO are not opposites. They are two retrieval systems sharing most of the same supply chain. Write the page so a human can find the answer in ten seconds, name your sources, keep your facts fresh, and the same page tends to do well in both. The piece of advice that's specifically new is the one about leading with the answer and quoting your evidence. Do that, and you stop optimising for one channel at the expense of the other.
