Search is splitting in two. There's the traditional results layer — ten blue links ordered by a ranking algorithm — and there's the AI-generated answer layer: ChatGPT, Perplexity, Google AI Overviews. GEO is the discipline of optimising for the second layer.
Classic SEO asks: "Can Google find and rank this page?" GEO asks: "Will an LLM cite this brand when answering a question?"
What is Generative Engine Optimisation?
GEO (Generative Engine Optimisation) is the practice of structuring your content, entity signals, and authority footprint so that large language models — and the retrieval systems they use — surface and cite your brand in generated responses.
The term was coined in academic research in 2023 and has since become the working label for a cluster of techniques that SEO professionals are still actively mapping. Unlike classic SEO, GEO doesn't have a single algorithm to reverse-engineer. Each AI system — Perplexity, ChatGPT, Gemini, Claude — makes different retrieval decisions. The common thread is entity trust and answer-worthiness.
How it differs from classic SEO
The table below shows where the signals diverge. Classic SEO and GEO share some foundations (structured data, E-E-A-T, content quality) but weight them differently, and GEO adds signals that classic SEO largely ignores.
| Signal | Classic SEO | GEO (AI Visibility) |
|---|---|---|
| Keyword in title | ✓ Critical | ✓ Helpful |
| Backlinks | ✓ Critical | ○ Indirect |
| Entity disambiguation | ○ Minor | ✓ Critical |
| Structured data (JSON-LD) | ✓ Helpful | ✓ Critical |
| Direct answer format | ✓ Snippet | ✓ Critical |
| Citation-worthy claims | ○ Indirect | ✓ Critical |
| Author E-E-A-T signals | ✓ Helpful | ✓ Critical |
| Page speed / CWV | ✓ Ranking | ○ Minor |
| Consistent brand mentions | ○ Brand | ✓ Critical |
The biggest conceptual shift: classic SEO optimises for a ranking position. GEO optimises for being a trusted source that an LLM references when constructing an answer. These aren't the same thing.
The five GEO levers
1. Entity clarity
LLMs maintain internal representations of entities — people, brands, organisations, locations. If your entity is ambiguous or thin, the model defaults to safer, better-known sources. The fix is entity disambiguation: consistent, unambiguous signals across your site, structured data, Wikipedia, Wikidata, social profiles, and third-party mentions.
2. Answer-formatted content
AI systems extract answers, not pages. Content that directly answers the question — in the first paragraph, with a clear claim, followed by supporting evidence — is structurally easier to cite. Long preamble, hedged conclusions, and buried lede all reduce citation likelihood.
3. Structured data (JSON-LD)
Schema markup is more important for GEO than for classic SEO. LLMs and their retrieval pipelines use structured data to understand what a page is about, who created it, and what claims it makes. Person, Organisation, FAQPage, HowTo, and Article schema are the most valuable.
4. Citation-worthy authority signals
Models cite sources they've seen cited by other sources. This creates a self-reinforcing loop: if authoritative publications reference you, models are more likely to reference you. Link building still matters — not because of PageRank, but because coverage in trusted publications trains models to associate you with credibility.
5. Consistent brand co-occurrence
Brand mentions — even unlinked — across relevant contexts train LLMs to associate your name with a topic. If "Abdallah Mekky" appears alongside "Technical SEO" and "GEO" across multiple credible contexts, models learn that association. This is why PR, thought leadership, and owned content all feed GEO.
Practical takeaway
Start with entity signals before content. Make sure your brand, name, and specialisation are unambiguously defined in your structured data, your About page, and at least one credible external source. Without entity clarity, no amount of answer-formatted content will reliably produce citations.
Which AI systems does GEO apply to?
Each system has its own retrieval behaviour:
- ▸Perplexity — real-time web retrieval. Responds well to high-quality, cited, structured content. Currently the most predictable for GEO optimisation.
- ▸Google AI Overviews — draws from the search index. Classic SEO signals still matter here, but answer-formatted content and schema give an edge.
- ▸ChatGPT / SearchGPT — mix of training data and Bing retrieval. Entity signals in training data (Wikipedia, credible mentions) matter more here than on-page optimisation.
- ▸Gemini — integrates Google Search and Knowledge Graph. Entity disambiguation and structured data are particularly effective.
- ▸Claude (Anthropic) — primarily training-data based, with some retrieval. Consistent, authoritative, widely-cited content matters most.
What GEO is not
GEO is not prompt engineering. It's not stuffing your content with phrases you hope AI systems will copy. It's not a content volume play. And it's not separate from your existing SEO — it layers on top of a solid technical and content foundation. If your site has crawl issues, thin content, or no structured data, GEO techniques will have limited effect.
Fix the foundation first. GEO amplifies a strong signal — it doesn't manufacture one.
Where to start
- ▸Audit your entity signals: How does ChatGPT describe you? What does Perplexity say? Does Gemini know who you are?
- ▸Implement
PersonorOrganisationJSON-LD on your homepage and About page. - ▸Add a clear, answer-formatted definition of your core service on your homepage.
- ▸Get one credible external citation — a feature, interview, or industry mention — that confirms your entity.
- ▸Add
llms.txtto your site root to help AI crawlers parse your structure efficiently.
GEO is early. The techniques that work today will evolve as retrieval systems mature. But the underlying principle won't change: be a trustworthy, unambiguous, citable source — and AI systems will treat you as one.