What is generative engine optimization (GEO)?

Definition

Generative engine optimization (GEO) is an academic and practical discipline concerned with optimising web content so that it is cited and used by generative AI systems – primarily large language models (LLMs) such as ChatGPT, Gemini, Perplexity and Claude.

What is GEO – and where the term actually comes from

The term GEO (generative engine optimization) was formally introduced in November 2023 in an academic paper by Aggarwal et al., a team of researchers from IIT Delhi, Princeton University, Georgia Tech and the Allen Institute for AI. The paper was published on arXiv (2311.09735) and presented at KDD 2024 – one of the most respected conferences in data mining and machine learning.

Importantly, GEO is not a marketing term coined by the industry. It is a scientific framework with a defined benchmark (GEO-bench), reproducible experiments and quantifiable results.

GEO-bench in brief:

• 10,000 search queries spread across a broad range of subject areas
• 9 optimisation strategies tested systematically
• Validated on real traffic in Perplexity.ai
• Results measured with PAWC (Position-Adjusted Word Count – the study's own measure of how much of an AI answer's wording comes from a given source, weighted by its position in the answer)

GEO vs AEO – two lenses on the same phenomenon

A common source of confusion is the relationship between GEO and AEO. They are not synonyms, but they overlap considerably.

AEO (Answer Engine Optimization) is the umbrella term. It covers optimising to be chosen as the answer in every AI-driven search and answer system – including Google AI Overviews, voice assistants and hybrid search experiences. AEO is the practical, broad perspective.

GEO is the academic and technical lens. It focuses specifically on generative LLM-based engines: ChatGPT, Gemini, Perplexity, Claude. GEO gives us the empirical basis – it is the research that explains why AEO techniques work, and in which contexts.

In short: AEO is what you do. GEO is why it works.

How generative engines choose sources: RAG + LLM mechanics

To understand GEO, you need to understand the technical process behind AI answers. Most modern generative search engines – Perplexity, Gemini, ChatGPT with web search – use an architecture called RAG: retrieval-augmented generation.

The process has two distinct steps:

1. Retrieval – fetching the information
The system searches external sources and fetches the documents judged most relevant to the question. Technical SEO, crawlability and the structure of the content play a decisive role here. Content that is not found in this step can never be chosen in the next.

2. Generation – producing the answer
The retrieved information is used as context when the language model formulates its answer. This step decides which sources are actually cited, how they are paraphrased and whether they are visible to the user.

The AI system does not make up answers – it summarises and synthesises existing content. GEO aims to make sure your content is what gets chosen in the generation step, not merely found in the retrieval step.

The three techniques the research shows work

Aggarwal et al. tested nine optimisation strategies in the GEO study (arXiv:2311.09735). Three stood out with a consistent, measurable effect:

1. Cite sources – reference where your information comes from

Explicitly citing external sources – studies, reports, official data – gave a ~28% PAWC improvement on GEO-bench. The effect is most dramatic for lower-ranked pages: for websites in position 5 in the SERP, the study measured visibility increases of up to +115% (Table 2 in the study). The logic is straightforward: an AI system trying to minimise the risk of wrong answers prefers content that shows where its information comes from.

2. Add statistics – include quantitative data

Quantitative claims – percentages, measurements, comparative figures – gave a ~33% PAWC improvement on GEO-bench. On real traffic through Perplexity.ai, the study measured a +37% improvement in subjective impression (Table 5 in the study). Specific figures are easier for an AI system to extract and use directly in an answer.

3. Expert quotes – quotes attributed to named experts

Quotes with clear attribution – name, title, organisation – gave the highest PAWC improvement of all the techniques tested: ~43% on GEO-bench, confirmed by +22% in the Perplexity validation (Table 5). The effect is especially strong on opinion-based and historical topics.

Bonus effect: fluency
Well-written, clear prose gives a ~29% PAWC improvement – and the effect is amplified when combined with the techniques above. Clarity is not just about aesthetics; it is a ranking factor in generative systems.

What does not work – and why it matters

Knowing what does not work matters as much as knowing what does – especially when it contradicts long-held SEO assumptions.

Keyword stuffing reduces visibility
Traditional keyword optimisation – repeating the target keyword frequently in the text – did not just have no effect. It reduced visibility to about 10% below the baseline. Generative systems read keyword overuse as a quality problem, not a relevance signal.

llms.txt has no measurable effect
llms.txt is a file some people advocate as a way to speak directly to AI crawlers. More recent data confirms that the file has no measurable effect on citation rates. The same goes for FAQ schema as an isolated technique – structured data does not help if the underlying content lacks substance.

GEO rewards quality and credibility. It penalises techniques that try to sidestep the actual value of the content.

GEO in practice: a checklist for companies

A working checklist based on the combined research, for use in content reviews and publishing.

Content and structure

• Every factual claim is tied to a named source (study, report, public authority)
• At least one quantitative data point per main section (percentage, measurement, volume)
• Expert quotes include name, title and organisation
• The text is written in clear, coherent prose – fragmented bullet lists are never the only format
• The content covers the subject broadly enough to fit into a semantic cluster

Credibility and E-E-A-T

• The author's background is clear (byline, bio or about page)
• The content has been updated within the last 90 days (or shows a visible update date)
• The page is linked from related pages within the same topic cluster
• No misleading claims or unattributed generalisations

Technical base

• The page is indexable and crawlable (check robots.txt and canonical)
• Load time is acceptable and Core Web Vitals pass
• The content is in a format that is easy to extract: clear headings, short paragraphs, defined terms

How to measure GEO success

Measuring GEO is different from measuring traditional SEO. There is no standardised tool like Google Search Console for AI citations yet – but there are methods.

Manual citation tracking
Regularly put your most important questions to ChatGPT (with web search enabled), Perplexity and Gemini. Record whether and how your website is cited. It is time-consuming but gives you direct insight.

Tool-based monitoring
Tools such as SE Ranking, Semrush and Otterly are starting to offer AI citation tracking. Perplexity.ai often displays its sources visibly, which makes them easier to track than ChatGPT's.

Metrics to track:

• Number of citations in Perplexity per month (per topic cluster)
• Share of answers in which your domain is named
• Change in branded search volume (an indirect measure of AI-driven visibility)
• Referral traffic from AI platforms in Google Analytics 4

Measuring GEO takes a combination of manual review and tool data – at least until the industry has standardised its measurement methods.

Why content freshness matters

AI systems have a built-in preference for fresh content. According to Lily Ray & Amsive (March 2026), 50% of all AI-cited content is less than 13 weeks old. So a post from last quarter is already starting to age out of the AI citation window. Regular updates – with new source references, statistics and relevant examples – are among the most concrete steps a company can take to improve its GEO visibility.

When to use which perspective – GEO, AEO or traditional SEO

The three perspectives do not compete – they are complementary layers in a modern content strategy.

Traditional SEO is the foundation. Without technical SEO, crawlability and indexing there is nothing for AI systems to fetch. SEO is a prerequisite, not an alternative.

GEO is the academic and technical perspective. Use GEO research when making decisions about content formats, source references and credibility markers. GEO gives you the empirical basis for why specific techniques work in LLM-based systems.

AEO is the operational umbrella perspective. Use AEO when you plan a broader strategy that includes Google AI Overviews, voice assistants and hybrid search experiences – not just LLM chats.

SEO makes your content findable. GEO explains what gets it chosen. AEO is the strategy that ties it all together.

Sources

• Aggarwal et al. (2023). GEO: Generative Engine Optimization. arXiv:2311.09735. KDD 2024. IIT Delhi, Princeton University, Georgia Tech, Allen Institute for AI.
• Wu et al. (2026). AutoGEO. Carnegie Mellon University. ICLR 2026.
• Lily Ray & Amsive (2026). Content Freshness and AI Search. March 2026.