Search engine optimisation (SEO) is the practice of making content rank in Google. Generative engine optimisation (GEO) is the practice of making content cited in AI answers.
They share some foundations — quality content, clear structure, credible sources. But they diverge in important ways, and the divergence is growing as AI answer engines become a mainstream first step in the information journey.
How AI engines decide what to cite
AI answer engines draw on two mechanisms: training data and real-time retrieval.
Training data is the corpus the model was trained on. Content that appeared frequently, was cited often, and was associated with accurate, well-structured information is more likely to be represented in the model's internal knowledge. This is slow-moving — it changes on training update cycles, not in real time.
Retrieval-augmented generation (RAG) allows AI engines to pull live content at query time. Perplexity does this by default. ChatGPT does it when web search is enabled. Claude does it in some contexts. The content retrieved is processed, summarised, and potentially cited.
For retrieval, the signals that matter are different from Google's ranking signals. They include:
- Direct answer structure — content that answers the question in the first paragraph, without preamble
- Entity clarity — named entities, clearly attributed claims, specific data points
- Structured content — FAQ sections, numbered lists, clear headings that match likely query phrasing
- Credibility signals — author credentials, publication dates, external citations, E-E-A-T signals
- Technical accessibility — fast page load, clean HTML, absence of blocks to known AI crawlers
How GEO differs from SEO
| SEO | GEO | |
|---|---|---|
| Primary signal | Backlinks, authority, relevance | Content structure, entity clarity, E-E-A-T |
| Update cycle | Continuous (crawl-based) | Training updates + real-time RAG |
| Primary output | Ranking position | Citation in answer |
| Measurement | Click-through from SERP | Citation appearance + referral |
| Key tactics | Link building, keyword targeting | Answer-first structure, FAQ schema, entity enrichment |
The five GEO mechanisms that actually move the needle
1. Answer-first structure
Put the direct answer to the question in the first sentence. AI engines summarise content — content that opens with the answer is more likely to be accurately summarised and cited.
2. FAQ schema
Structured FAQ schema (in JSON-LD) makes question-and-answer pairs explicitly machine-readable. This is one of the strongest retrieval signals for AI engines using RAG.
3. Entity enrichment
Named entities — people, organisations, products, places — that are clearly attributed and consistently named improve entity recognition. If your brand is mentioned in the same context as well-known entities in your space, that association is more likely to surface in AI answers.
4. llms.txt
A plain-text index of your most important pages, in a format readable by any LLM. Placed at /llms.txt, it gives AI crawlers a curated map of your highest-signal content. Some AI platforms check for it explicitly.
5. Citation-ready claims
Quotable, specific, verifiable statements are more likely to be cited than vague assertions. "Conversion rates improved by 34% after implementing X" is citable. "Results may vary" is not.
Measuring GEO
SEO measurement is mature — rankings, organic traffic, click-through rate. GEO measurement is newer and requires different data sources.
DarkTraffiK's three-signal approach covers the full AI funnel: server-side crawler data (are AI engines reading your content?), scheduled citation checks (are they recommending it?), and GA4 referral data (are humans clicking through?).