Generative Engine Optimization (GEO) is the content and technical optimisation discipline aimed at increasing the likelihood that a website is shown as a source in the answers given by generative artificial intelligence search engines such as ChatGPT, Perplexity and Google AI Overviews. The concept entered the literature in November 2023 with an academic paper published by researchers from Princeton University, Georgia Tech, the Allen Institute for AI and IIT Delhi. Classic SEO aims to rank at the top in Google's ranking algorithm; GEO targets which sources large language models (LLMs) select and cite when producing an answer.
Summary: GEO is the work of getting your brand shown as a source in the answers produced by AI-powered search tools. The 2023 Princeton-led research that defined the concept measured that source visibility in generative engine answers can be increased by up to 40% with the right techniques. Its core techniques: citable definitions, direct answer blocks, statistics with source attribution, structured data and entity consistency. GEO does not replace classic SEO; it is built on top of a solid SEO foundation.
Why Did GEO Emerge?
GEO is the answer to search behaviour shifting from links to AI-generated answers. Users now get the answer to their question not from ten blue links but from the answer ChatGPT, Perplexity or Google AI Overviews synthesises into a single piece of text. A brand that does not appear as a source in that answer is invisible to the user at that moment of search.
The scale of the behavioural shift is not an estimate but published data:
- OpenAI CEO Sam Altman announced in October 2025 that ChatGPT had reached 800 million weekly active users.
- Google stated at its second-quarter 2025 investor meeting that the AI Overviews feature had reached 2 billion monthly users.
- In a forecast published in February 2024, Gartner predicted that traditional search engine volume would fall 25% by 2026 because of AI chat assistants.
The concept's academic origin is clear too: the paper "GEO: Generative Engine Optimization", uploaded to arXiv by Pranjal Aggarwal and colleagues on 16 November 2023 and later published at the KDD 2024 conference, named the discipline, built the 10,000-query GEO-BENCH data set and measured the visibility effect of nine different content strategies in generative engine answers. The paper's core finding: with the right optimisation, source visibility can be increased by up to 40%, and the effect varies by domain — meaning a sector-specific approach is needed.
How Does GEO Work?
Generative search engines work in three stages: they retrieve the sources relevant to the query, synthesise a single answer from those sources, and generally attribute the sources they used. GEO makes content fit to be selected at each of those three stages: first findable, then citable, and finally worth attributing.
The four signals that stand out in LLMs' source selection are:
- Citability: the model prefers passages that are complete in themselves and hold their meaning when taken out of context. If a paragraph gives the answer to the question on its own it is easy to synthesise; content whose answer is spread across three paragraphs is filtered out.
- Information density: text containing concrete data, dates, figures and named concepts carries more "extractable information" than generic text of the same length. Filler sentences are no use to the model.
- Structured content: heading hierarchy, tables, lists and schema markup let content be parsed and classified correctly by machines. The model reads what is a definition, what is a step and what is a comparison from the structure.
- Authority and consistency: sites that show their sources, that have a named author and that give consistent entity signals across the web are carried into answers with greater confidence. Because most generative engines are fed by classic search indexes, existing domain authority is also indirectly in play.
GEO Techniques
GEO techniques turn content into a structure large language models can easily extract and cite. The methods that stood out in the Princeton research were adding statistics, citing trustworthy sources and using quotations; keyword stuffing, meanwhile, was measured among the tactics that do not work in generative engines. The table below summarises the core techniques applied in practice.
| Technique | What it does |
|---|---|
| Direct answer blocks | Gives the clear answer to the question in the first 40–60 words under every heading; produces a passage the model can cite without losing context |
| Definitional opening | A first sentence in the "X is …" pattern becomes the source a model can pass on verbatim for definition questions |
| Statistics with source attribution | Verifiable figures and attribution increase the likelihood of content entering answers; among the most effective methods in the research |
| Structured data (schema) | JSON-LD markup such as Article, FAQPage and Organization lets content be classified correctly by machines |
| Tables and lists | Turns comparison and step data into a machine-readable format; preferred over plain text during synthesis |
| llms.txt | The site's summary map aimed at LLMs; presents which page covers what to the model in a single file (a standard proposed by Jeremy Howard of Answer.AI in September 2024, still at the proposal stage) |
| Entity consistency | The brand name, field of expertise and identity details being consistent within the site and across the web lets the model recognise the brand with confidence |
| TL;DR / summary blocks | A dense summary at the start of long content offers a condensed answer the model can cite in a single pass |
What these techniques have in common is this: none of them inflates content with trickery for the sake of artificial intelligence; they all present information more clearly, with more sources and more structure. Good GEO also means better content for the human reader.
The Relationship Between GEO and Classic SEO
GEO is not classic SEO's rival but a layer built on top of it. Generative engines draw their sources largely from existing search indexes; a site that cannot be crawled, is not indexed or has no authority cannot enter AI answers either. Without a solid SEO foundation, GEO work hangs in the air; the two disciplines are therefore run as parts of a single strategy.
In practice the difference is this: SEO optimises the question "where do I rank", GEO the question "am I inside the answer". The third member of this family, AEO (Answer Engine Optimization), focuses on answer engines including answer boxes and voice assistants. The differences between the three approaches in scope, technique and measurement are a separate subject; this page's subject is GEO itself.
Who Should Take GEO Seriously?
Every business whose customers research before buying should put GEO on their agenda. In sectors where the decision process rests on asking questions — services, health, finance, software, education — a portion of user questions now goes to artificial intelligence tools; a brand that does not appear in the answers never enters the user's evaluation list at all.
A concrete framework for prioritisation:
- B2B service and consultancy firms: queries of the "how do I choose the best X agency", "what does X service cover" type are now frequently asked of AI tools; the firms named in the answer make the shortlist.
- SaaS and software companies: in product comparison and "which tool for X" queries, AI answers effectively work like a recommendation engine.
- YMYL fields such as health, law and finance: models are more conservative in source selection in these fields; a small number of sites with strong authority and source attribution can dominate the answers.
- E-commerce: in product category and buying guide queries, AI Overviews visibility creates a shop window independent of classic rankings.
- Local service businesses: in "near me / in the city" type queries, niche and local content can gain AI visibility far faster than broad queries.
The common rule: the more niche the query, the higher the chance a single well-prepared piece of content enters the answer.
The GEO Opportunity in Türkiye
GEO in Türkiye is currently a field that rewards moving early. In Turkish-language queries, the supply of quality, structured, source-citing content models can choose from is markedly narrower than in English; a small number of well-prepared sources can take a disproportionate share of visibility in the answers. That window will stay open until the competition intensifies.
The picture we see again and again in our own site audits supports this: on Turkish sites llms.txt is almost never used, structured data is mostly missing or incorrect, robots.txt policy aimed at AI bots is undefined and content is not structured to answer directly. That is, the technical bar is low; a site that completes basic GEO preparation becomes one of the rare sources models can "choose" in its own niche. In English-language markets that advantage has largely closed; in Turkish it has not closed yet.
Overstatement should be avoided here: GEO is no magic wand in Türkiye either. Weak content does not enter answers through technical preparation. The opportunity is the possibility that the combination of "low competition + a low technical bar" delivers faster-than-usual results for a business producing quality content.
Frequently Asked Questions
Will GEO replace SEO? No. Generative engines draw their sources largely from classic search indexes; a site that is not indexed cannot enter AI answers either. Google search is still the main discovery channel, and even the 25% drop Gartner forecasts for 2026 means three quarters of the volume stays in classic search. The right setup is running the two under a single strategy.
Can GEO results be measured? Yes. The basic method is putting a set of questions specific to your sector to ChatGPT, Perplexity and Google AI Overviews periodically and counting in how many answers your brand appears as a source (citation tracking). Referral traffic from sources such as chatgpt.com and perplexity.ai is added to that on the analytics side. A before/after comparison shows the work's impact concretely.
What is the difference between GEO and AEO? GEO focuses on being a source in the answers generative artificial intelligence engines synthesise; AEO on being the direct answer in answer engines including answer boxes and voice assistants. Their scopes largely overlap and their techniques are shared.
How long does GEO take to show results? It varies by query type. In niche, local and Turkish-language queries, well-prepared content can start appearing in answers within weeks; broad, competitive "best X" type queries require authority building and take months. An approach that guarantees a timeframe is hiding that distinction.
Which content gets cited more in AI answers? Content that opens with a clear definition, answers questions directly under the heading, holds verifiable statistics and sources, and uses tables and lists. The Princeton research measured adding statistics and citing sources among the methods that increase visibility most; it showed keyword stuffing to be ineffective.
Is adding llms.txt compulsory? It is not compulsory; it is a standard proposed in September 2024, not yet official, and how far engines use it is debated. But it is low cost, does no harm and has spread rapidly in the documentation ecosystem. We recommend adding it as a low-cost preparation step; we do not regard it as sufficient on its own.
The Next Step
Building a strategy without measuring your site's current visibility in AI answers and its technical GEO readiness is working blind. At DijitalPi we take the snapshot first — which questions you appear in, where your competitors are named, how ready your site is technically — then draw up the priority action list. Take a look at our AI digital marketing service or, to strengthen the classic foundation, look at our SEO services.
References
- Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., Deshpande, A. "GEO: Generative Engine Optimization", arXiv:2311.09735, November 2023 (KDD 2024) — arxiv.org/abs/2311.09735
- TechCrunch, "Sam Altman says ChatGPT has hit 800M weekly active users", October 2025 — techcrunch.com
- TechCrunch, "Google's AI Overviews have 2B monthly users", July 2025 — techcrunch.com
- Gartner, "Gartner Predicts Search Engine Volume Will Drop 25% by 2026, Due to AI Chatbots and Other Virtual Agents", February 2024 — gartner.com
- Howard, J. (Answer.AI), "/llms.txt — a proposal to provide information to help LLMs use websites", September 2024 — llmstxt.org
