1) What is GEO vs. SEO?
- SEO (Search Engine Optimization): optimizing content and sites to rank and earn clicks from search engine results pages (SERPs).
- GEO (Generative Engine Optimization): optimizing content to be referenced, cited, or used inside AI-generated answers across engines like ChatGPT, Gemini/AI Overviews, Copilot, Perplexity, Arc Search, etc. The concept was formalized in late-2023 academic work introducing a creator-centric framework to improve “visibility in generative engine responses.”
Plain-English difference: SEO tries to win the SERP click; GEO tries to win the mention inside the answer. Industry primers now define GEO as improving visibility in AI-driven experiences (ChatGPT, Perplexity, Gemini, Copilot, Google AI Overviews).
2) Why GEO now?
- AI Overviews and answer engines are changing discovery and reducing traditional clicks, pushing brands to optimize for being cited in answers rather than only ranking. Leading coverage and analyses across 2024–25 draw the same conclusion: optimize for generative interfaces, not just blue links.
- A top-down view from a16z: LLMs prioritize content that’s well-organized, easy to parse, and dense with meaning (not just keywords). Phrases like “in summary” and bullet points help LLMs extract and reproduce content. Also, the metric that matters becomes reference rate (how often models cite you).
AI Overviews and the Publisher Dilemma
- AI Overviews are Google’s generative summaries that appear at the top of search results, pulling content from multiple publishers into a single answer box. While convenient for users, new research shows they are cutting publisher clickthrough rates by up to 50%. This decline has sparked legal complaints, as shown in the screenshot below, highlighting the urgent need for businesses to adapt with strategies like GEO.
3) GEO & SEO are complementary (not rivals)
Two current viewpoints you should know:
“Good SEO is good GEO.” — Danny Sullivan, Google (WordCamp US, Aug 28, 2025).
“To get your content to appear in AI Overview, simply use normal SEO practices. You don’t need GEO, LLMO or anything else.” — Gary Illyes, Google (Jul 24, 2025).
Takeaway: Excellent SEO, clear, unique, well-structured, well-cited content, transfers powerfully to GEO. That said, GEO adds answer-centric formatting and model-friendly signals that most SEO playbooks under-use.
4) Key differences at a glance
|
Dimension |
SEO |
GEO |
| Primary surface | Classic SERPs | AI answers (chat, overviews, summaries) |
| Success metric | Rankings, organic clicks | References/mentions in answers; cited links |
| Signals emphasized | Topical authority, links, intent match, CWV | Parseability, concise facts, structured data, citation-friendly formatting, reference rate |
| Content shape | Long-form pages that satisfy intent | Answer-ready modules: TL;DR, bullet lists, tables, concise claims with sources |
| Related frameworks | E-E-A-T, AEO (featured snippets) | GEO, AEO (answer/featured-snippet), AIO/AI visibility optimization |
Good industry pieces summarize the overlap and distinctions among AEO, GEO, SEO—AEO focuses on featured/answer boxes; GEO extends to AI chat/agents.
5) Advanced GEO tactics you rarely see elsewhere
These go beyond most “GEO vs SEO” posts and directly target how LLMs parse, ground, and cite content.
- Answer-forward formatting (make extraction trivial)
- Add TL;DR blocks, checklists, bullet lists, and contrast tables near the top. Use clear section headers (“In summary”, “Key facts”, “Pricing at a glance”). This improves “chunkability” and citation likelihood.
- Proximity citations
- Place source links immediately next to factual claims (not at the bottom), mirroring how LLMs learn to attribute. This boosts the odds your URL appears as the cited source in answers. a16z frames the KPI as reference rate—optimize for being cited.
- Structured data for answers
- Mark up FAQPage/HowTo for Q&A answers, Product with rich attributes, Organization/Person, and even Award schema for “best/award-winning” queries (useful for AI Overviews).
- LLM-friendly chunking (for your own KB/RAG and third-party ingestion)
- Keep sections coherent and scoped; common RAG best practice is ~128–512 tokens with ~10–15% overlap; AWS Knowledge Bases default around ~300 tokens. This is about making your content easier to retrieve & quote accurately (by your systems and others).
- Unique data & originals
- Publish original stats, tables, and definitions with explicit units and dates; LLMs prefer canonical facts they can lift verbatim. (This is also classic SEO.)
- Stable anchors & permalinks
- Give important sections named anchors (e.g., /geo-vs-seo#metrics) so AI engines can deep-link. This improves traceable citations and user trust (and reduces hallucinations).
- Govern crawling choices for AI
- Understand bot controls: Google-Extended lets you opt out of Gemini training/grounding; GPTBot (OpenAI), PerplexityBot, ClaudeBot can be allowed/blocked via robots.txt (business tradeoff: AI visibility vs. data control). Note: Google says it doesn’t use LLMs.txt for AI Overviews; normal SEO indexing still matters. Recent reports also flagged “stealth crawling” controversies to watch.
- Answer clustering > single keywords
- Build clusters around question variations (who/what/when/how/versus/pros-cons). GEO and AEO both reward breadth of well-structured answers within a topic cluster.
6) Measurement that matters (GEO KPIs + tools)
Track both classic SEO and GEO-specific visibility:
- Reference/Mention Rate in AI answers (how often models cite you, and on which prompts). Industry analysis highlights this as a core metric for generative interfaces.
- AI Overview / AI answer share (Are you cited in Google AI Overviews? Which URLs are used?) Tooling and platforms are emerging fast; even Wix launched AI Visibility Overview for tracking AI citations & query volume.
- Accuracy & sentiment of AI mentions (are facts right? how are you framed?). a16z highlights new dashboards benchmarking model outputs and brand perception across engines.
- SERP health (rankings, clicks, CVR, CWV). Yes, still essential for discovery—and it helps GEO. Google has repeatedly said “normal SEO” is what gets content into AI Overviews.
7) 30-Day Dual-Optimization Plan (SEO → GEO lift)
Week 1 — Discovery & baselines
- Map top 25 revenue/impact queries. Pull SERP data (rank/clicks) + AI answer presence/citations baseline.
- Content audit: identify 10 pages to become answer hubs (quick facts, bullets, tables, FAQ).
Week 2 — Restructure for answers
- Add TL;DR, Key facts, and FAQ blocks to each page; place source links beside claims.
- Implement FAQPage/HowTo/Product/Award schema where relevant.
Week 3 — Data & chunkability
- Publish at least one original table/mini-study per hub with clear units, dates, and downloadable CSV/JSON.
- Refactor long pages with sub-headers every ~200–300 words; keep sections coherent; if you run RAG internally, target ~128–512 token chunks with ~10–15% overlap.
Week 4 — Bot policy & monitoring
- Decide AI bot posture (allow/limit GPTBot, PerplexityBot, ClaudeBot; consider Google-Extended).
Set up AI visibility tracking (e.g., Wix AI Visibility, or internal trackers); review reference rate, accuracy, and sentiment weekly; iterate.
8) FAQs
Is GEO replacing SEO?
No. It’s an expansion: SEO gets crawled/indexed; GEO gets cited in AI answers. Many experts (and Google staff) emphasize good SEO ≈ good GEO.
Should I chase LLMs.txt?
For Google, no Google doesn’t use LLMs.txt for AI Overviews today; “do normal SEO.” Other engines may experiment, but don’t expect near-term impact.
Where does AEO fit?
AEO is “answer engine optimization”—historically about featured snippets & direct answers in classic SERPs. GEO extends answer-readiness to AI chats & overviews. Use both.
Final take
If you structure content like answers, cite sources near claims, mark up with the right schema, and keep SEO fundamentals strong, you will earn both: SERP rankings and LLM citations. That’s GEO × SEO working together.
