Most people tell me GEO is just "SEO with extra steps." I disagree.

When I started digging into how ChatGPT, Perplexity, and Google's AI Overviews actually decide which websites to cite, I realized something that changed my entire approach. Generative Engine Optimization isn't a marketing problem. It's a data science problem.

You're not trying to climb a ranked list. You're trying to get inside the reasoning process of an AI system and influence the parameters that determine whether your brand shows up in its answer or gets ignored entirely.

"GEO is like getting into the brain of AI. Making AI think what we want it to think. If we want to push into the brain in a certain way, we need to influence various parameters which are under our control - which in turn change the way an AI system evaluates us."

That shift in mental model - from "ranking on a page" to "entering the AI's reasoning" - is what this guide is about. Whether you are building a GEO strategy framework from scratch or refining an existing answer engine optimization approach, this is where the fundamentals start.

What Is Generative Engine Optimization (GEO)? [toc=GEO Definition]

Generative Engine Optimization (GEO) is the practice of optimizing your content so that AI-powered search platforms - ChatGPT, Google AI Overviews, Perplexity, Claude, and Gemini - cite, reference, or feature your website when generating answers to user questions. Instead of competing for a slot among ten blue links, GEO focuses on making your content the trusted source AI engines draw from when they synthesize responses.

🔑 The Origin of the Term

The term was formally introduced in November 2023 by a research team led by Pranjal Aggarwal at Princeton University. Their paper, "GEO: Generative Engine Optimization," was published at the ACM SIGKDD Conference (KDD 2024) - one of the most respected venues in computer science.

This wasn't some marketing agency coining a buzzword. This was peer-reviewed academic research. The team built GEO-bench, a benchmark of over 10,000 diverse queries, and tested nine different optimization strategies to see what actually moves the needle on AI visibility.

The Headline Finding

Their headline finding? Specific content optimization strategies can boost visibility in AI-generated responses by up to 40%.

I'll break down exactly which strategies those are in a later section. But the key point is this: GEO is grounded in empirical research, not guesswork.

💡 What Makes GEO Different

Here's the mental model I use. Traditional SEO asks: "How do I rank higher on a list?" GEO asks a fundamentally different question: "How do I become the source the AI trusts enough to quote?"

The distinction matters because AI engines don't just show your website. They read it, extract passages, and weave them into a new answer - often quoting you directly. Google's own research team at DeepMind built a system called SEMQA that combines "factual quoted spans - copied verbatim from given input sources - and non-factual free-text connectors that glue these spans together".[tryprofound]​

Self-Standing Content Is Non-Negotiable

That means your content needs to contain self-standing, quotable passages. If a paragraph only makes sense with three paragraphs of context above it, AI won't extract it.

"Instead of trying to be in the answer, we're trying to become the answer. By becoming the most trusted source for AI."

This is the core of what I believe about GEO. It's not about gaming an algorithm. It's about building the kind of trust that makes AI engines want to cite you - because their own credibility depends on it. That trust-first philosophy is central to how we approach GEO content optimization at every level.

📖 Deep Dive: A full exploration of GEO's definition, framework, and foundational research → /ai-search-101/geo/fundamentals/what-is-geo/ [link pending]

How Do Generative Engines Actually Work? [toc=How AI Engines Work]

Generative engines - including ChatGPT Search, Google AI Overviews, and Perplexity - use a process called Retrieval-Augmented Generation (RAG) to answer questions. The AI first searches the web for relevant sources, retrieves specific passages from those pages, then uses a large language model to synthesize those passages into a coherent, original response with inline citations. Understanding this pipeline is the foundation of all GEO strategy.

🔑 The RAG Pipeline in Plain Language

Think of it like a research assistant with superhuman speed. When you ask Perplexity "What's the best CRM for startups?", here's what happens in milliseconds:

1. Query Processing: The AI interprets your question and generates multiple search queries behind the scenes.
2. Retrieval: It searches an index (Google uses its own web index; ChatGPT uses Bing's; Perplexity has a proprietary 200+ billion URL index) and pulls back relevant documents.
3. Extraction: It identifies the most relevant passages from those documents - specific paragraphs, data points, and quotes.
4. Generation: The LLM synthesizes those extracted passages into a new, cohesive answer with inline citations linking back to the sources.

This is the RAG framework, and it has progressed through three generations since 2023 - from Naive RAG (simple retrieve-and-generate) to Advanced RAG (with re-ranking and query refinement) to Modular RAG (with interchangeable components).[getpassionfruit]​

⚙️ Query Fan-Out: How Google's AI Thinks

Here's something most marketers miss entirely. When you type a question into Google's AI Mode, Google doesn't run one search. It runs dozens simultaneously.

Google calls this "query fan-out." Elizabeth Reid, Google's VP and Head of Search, described it at Google I/O 2025: "AI Mode uses our query fan-out technique, breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf".[liner]​

What the Patent Reveals

Google's patent for this system (US20240289407A1, "Search with Stateful Chat") reveals even more detail. The system maintains a context engine that tracks the user's entire search session. It generates "synthetic queries" - reformulated versions of the user's question - and runs them in parallel. It then annotates the AI's response with confidence scores and "linkifies" claims back to source documents.

What This Means for Your Content

If Google breaks one question into eleven sub-queries (which has been observed in practice), your content needs to be semantically rich enough to match multiple reformulated queries, not just the literal search term the user typed.

This is why thin, single-keyword content fails in GEO. And it's why the hub-and-spoke architecture you're reading right now is structurally aligned with how these AI systems actually work. Building GEO topic clusters around comprehensive semantic coverage is how you match this fan-out behavior.

📊 How AI Overviews Assemble Answers

There's a specific paper from Google Research that I think every GEO practitioner should know about: SEMQA (Semi-Extractive Multi-Source Question Answering) by Schuster et al., published at NAACL 2024.[tryprofound]​

SEMQA reveals that AI systems combine verbatim quoted spans copied directly from source pages with free-text connectors that the AI writes to glue those quotes together. It's not paraphrasing your entire page. It's cherry-picking specific sentences and stitching them into a new narrative.

Semantic Chunking in Practice

The practical takeaway? Every paragraph you write should express one complete idea. It should make sense if someone - or some AI - pulls it out of context. I call this "semantic chunking."

📖 Deep Dive: The full technical breakdown of RAG, query fan-out, and answer synthesis mechanics → /ai-search-101/geo/fundamentals/how-generative-engines-work/ [link pending]

What's the Real Difference Between GEO and SEO? [toc=GEO vs SEO]

GEO and SEO share a common foundation - both require high-quality content, technical accessibility, and authority signals. The critical difference is the end goal: SEO optimizes for ranking positions on a search results page, while GEO optimizes for being cited and quoted inside AI-generated answers. GEO builds on SEO but adds new requirements around content structure, source authority, and multi-platform visibility.

💡 The Fundamental Shift

Here's a parallel that helps me explain this. In traditional SEO, your content is a book on a library shelf. The librarian (Google) points people to the right shelf. But in GEO, your content is a source that a journalist (the AI) quotes in their article.

You're not competing for shelf position anymore. You're competing for citation worthiness.

That's a different game. A book can be mediocre and still sit on the right shelf. But a journalist will only quote sources that are credible, specific, and say something worth repeating.

📊 Side-by-Side Comparison

We break this comparison down in full detail in our GEO vs traditional SEO comparison guide.

✅ SEO Is Still the Foundation

I want to be clear here: GEO does not replace SEO. Google still sends 345x more traffic than all AI platforms combined as of late 2025. Your organic search foundation is the launchpad for GEO visibility.

Google's own guidance, published in May 2025, makes this explicit: "Focus on making unique, non-commodity content that visitors from Search and your own readers will find helpful and satisfying. Then you're on the right path for success with our AI search experiences".[hostinger]​

SEO Is the Qualifying Round

The analogy I keep coming back to is that SEO is the qualifying round. You need strong organic fundamentals to even enter the GEO competition. But once you're in, the rules change.

The Information Gain Advantage

There's also a data point from Google's Information Gain patent (granted June 2024) that I find fascinating. It calculates an "Information Gain" score to rank pages based on how much new information they provide relative to what the user has already seen.[openreview]​

In the GEO context, this means recycled content that restates what ten other blogs have already said will score low. Content with original data, unique frameworks, or novel analysis will score high. [EXPERIMENT CANDIDATE]

📖 Deep Dive: A detailed breakdown of every dimension where GEO and SEO differ, with strategy implications → /ai-search-101/geo/fundamentals/geo-vs-seo/ [link pending]

How Is GEO Different from Answer Engine Optimization (AEO)? [toc=GEO vs AEO] [IMPROVED]

GEO and AEO are related but distinct disciplines. Answer Engine Optimization (AEO) focuses on winning direct answers within existing search interfaces - featured snippets, People Also Ask boxes, voice search responses. Generative Engine Optimization (GEO) goes further, targeting citation and inclusion within entirely AI-generated responses on conversational platforms like ChatGPT, Perplexity, and Claude. AEO optimizes for answer boxes; GEO optimizes for AI-written answers.

🔑 Where the Confusion Comes From

I see these terms used interchangeably everywhere. Even some respected publications blur the line. So let me draw it clearly.

AEO emerged as traditional search engines started answering questions directly. Featured snippets, knowledge panels, voice search answers - these are answer engine features. AEO is about capturing those. Dharmesh Shah (HubSpot co-founder) and Ethan Smith (Graphite CEO) have been vocal advocates of AEO as a discipline. We cover the full discipline in our answer engine optimization guide.

GEO targets a different output entirely: the generated response. When ChatGPT writes three paragraphs answering your question and cites five sources inline - those sources were selected through a fundamentally different process than a featured snippet. The AI is actively synthesizing, not just selecting a best-match paragraph.

📊 The Three-Way Distinction

For a deeper comparison of these disciplines, see our AEO vs SEO breakdown.

💡 My Take: Concentric Circles

Here's my current thinking, subject to change as the field evolves. I see these as concentric circles. Good SEO is the outer ring - the broadest requirement. AEO is the next ring in - you're optimizing for direct answer formats within search. GEO is the innermost ring - the most demanding, where you need to be trusted enough that AI will put its own credibility on the line by quoting you.

When to Prioritize Which

Here's the practical decision framework I use with clients:

• You're a startup with no organic presence: Start with SEO fundamentals. You can't win GEO without being indexed and crawlable. But simultaneously build a B2B SaaS AEO strategy focused on citation placements in third-party content.
• You rank on page 1-2 for core terms: Layer AEO on top. Structure content for featured snippets. Build FAQ schema. This is your bridge to GEO.
• You have strong organic authority and E-E-A-T signals: Go hard on GEO. Your content is already retrievable. Now make it extractable, quotable, and multi-platform.

The nuance from the research that makes this practical: an AEO-optimized answer typically needs to be concise - 40-60 words to match a featured snippet. A GEO-optimized source needs to be comprehensive enough that the AI can extract multiple passages across a multi-paragraph response.

That's why thin FAQ pages might win featured snippets but fail at GEO. The AI needs depth to draw from.

📖 Deep Dive: The complete breakdown of GEO vs. AEO, including when to use which strategy → /ai-search-101/geo/fundamentals/geo-vs-aeo/ [link pending]

Why Does GEO Matter Right Now? [toc=Why GEO Matters]

GEO matters because AI search has crossed the threshold from experiment to mainstream behavior. ChatGPT reaches over 800 million weekly users. Google AI Overviews appear in more than 16% of all searches. AI referral traffic to websites surged 357% year-over-year between June 2024 and June 2025. The question is no longer whether users are searching via AI - it's whether your brand is part of the answers they're getting.

📊 The Traffic Shift Is Real

Let me be honest about this: traditional Google search still dominates. By a lot. But the trajectory of AI search traffic is what should get your attention.

Here are the numbers that keep me up at night:

• ⭐ AI-referred traffic converts at 14.2% compared to Google organic's 2.8% - a 5x difference
• ⭐ Go Fish Digital documented 25x higher conversion rates from AI referrals within 90 days of implementing GEO
• ⭐ Ahrefs' internal data showed 23x higher conversion rates from AI visitors, who represented 12.1% of signups from only 0.5% of total traffic
• ⚠️ Google AI Overviews have reduced click-through rates by an estimated 34.5% for traditional organic results

The conversion rate data is staggering. And it makes intuitive sense when you think about it.

"When a user is searching for which is the best CRM, if ChatGPT tells your name, then ChatGPT puts its trust in you. The user is very, very likely to buy from you. This is very clear from the initial data - conversion rates from AI search traffic are 4-5x higher than traditional search."

Understanding the ROI of GEO initiatives requires tracking these conversion differentials closely.

⚠️ The Binary Game

This is where the stakes get real. In traditional search, you had 10 blue links across potentially hundreds of results pages. If you ranked #47, someone might still find you.

In AI search? It's binary. The AI mentions 5-10 sources. If you're not in that set, you don't exist in that user's evaluation.

Think about someone evaluating CRM software. There are hundreds of CRMs out there. But when they ask ChatGPT, the AI returns a curated list of maybe 10. That becomes the sample set - the only products the buyer will evaluate. If you're not in it, you're invisible.

As Dharmesh Shah put it: "Either you show up or you don't. If you're not in the actual citations in the answer that was given, you might as well not have played the game because there is no difference."

🔑 The Citation Compounding Effect

Here's a finding from academic research that nobody in the marketing world is talking about. Algaba et al. (NAACL 2025) discovered that LLMs mirror human citation patterns but with a more pronounced bias toward already-cited sources.[mangools]​

In plain language: AI has a "rich get richer" problem. Sources that are already cited frequently get cited even more. This creates a compounding advantage for brands that establish GEO authority early.

This is the Matthew Effect, applied to AI search. And it means the window for establishing your brand as a trusted source is narrowing. The longer you wait, the harder it becomes to break into the AI's preferred citation network. [EXPERIMENT CANDIDATE] We explore the implications of this bias more deeply in our piece on ethics and bias in GEO.

💰 What's at Stake

Let me connect this to business outcomes, because that's what actually matters.

If you're running a B2B SaaS company and your competitor is already being cited by ChatGPT when prospects ask about solutions in your category - they're building trust equity you can't buy with ads. The prospect hasn't even visited their website yet, and they already trust them because the AI trusted them first.

At MaximusLabs, we call this the trust transfer effect. When ChatGPT or Perplexity cites your company, the AI platform's credibility transfers to your brand. That's why AI-sourced visitors convert at such dramatically higher rates. They arrive pre-qualified, pre-convinced, and ready to buy. Our GEO case studies document this effect across multiple client engagements.

📖 Deep Dive: The complete business case for GEO with ROI framework and industry benchmarks → /ai-search-101/geo/fundamentals/why-geo-matters/ [link pending]

What Are the Research-Backed GEO Strategies That Actually Work? [toc=Proven GEO Strategies]

The most effective GEO strategies are adding citations to credible sources, including direct quotations from experts, and embedding specific statistics - these three methods achieved a 30-40% visibility improvement in the foundational GEO study by Aggarwal et al. (KDD 2024). Notably, traditional SEO tactics like keyword stuffing showed little to no improvement in generative engine responses, confirming that GEO requires a fundamentally different playbook.

📊 The Nine Methods Tested

The Aggarwal et al. study didn't just propose GEO as a concept. They ran rigorous experiments on nine distinct optimization methods across 10,000 queries in GEO-bench. Here's what they found:

We walk through each of these methods with implementation guidance in our GEO experimental techniques playbook.

❌ Why Keyword Stuffing Fails in GEO

This table should stop every SEO professional in their tracks. Keyword stuffing - the bread and butter of early SEO - actively decreases your visibility in generative engine responses.

The reason is architectural. Traditional search engines relied on keyword matching. Generative engines use language models that understand semantic meaning. They don't need you to repeat "best CRM software" twelve times. They need you to demonstrate expertise through evidence - citations, data, and expert voices.

🔑 The Power of Combining Strategies

Here's a detail from the paper that almost nobody mentions. When the researchers tested combinations of GEO methods, the best pairing - Fluency Optimization + Statistics Addition - outperformed any single strategy by an additional 5.5%.

Even more interesting: Cite Sources showed its greatest power in combination. Used alone, it ranked below Quotation Addition. But when paired with other methods, it averaged a 31.4% improvement - the highest synergistic effect of any method.

Layer, Don't Pick

My takeaway? Don't pick one strategy. Layer them. A paragraph that includes a specific statistic, cites its source, reads fluently, and uses precise terminology is doing four GEO methods at once. [EXPERIMENT CANDIDATE]

⚠️ Domain-Specific Variation

One finding from the research that I think is critically underappreciated: GEO method effectiveness varies significantly by domain.

For example:

• Cite Sources performs best for factual queries where verification matters
• Statistics Addition excels in Law and Government and Opinion-type content
• Quotation Addition dominates in People and Society, History, and Explanation domains
• Authoritative Tone only shows major impact in debate-style questions

This means there's no universal GEO formula. What works for a SaaS startup may not work for a healthcare provider's educational content. You need to test within your domain. [EXPERIMENT CANDIDATE]

💡 A Democratizing Force

There's one more finding that I find genuinely exciting. When all sources are GEO-optimized simultaneously, lower-ranked websites benefit disproportionately more. The study showed that Cite Sources led to a 115.1% visibility increase for websites ranked 5th in traditional search, while the top-ranked website's visibility actually decreased by 30.3%.

Traditional SEO advantages - massive backlink profiles, aged domains - don't carry the same weight in generative engines. Content quality and optimization can level the playing field. For smaller brands and startups, this is a genuine opportunity.

📖 Deep Dive: A full exploration of GEO's definition, framework, and foundational research → /ai-search-101/geo/fundamentals/what-is-geo/ [link pending]

How Do AI Search Engines Choose Which Sources to Cite? [toc=AI Source Selection]

Each AI platform selects sources through its own retrieval pipeline, but the general process follows a pattern: retrieve candidate pages from a web index, filter for quality and authority signals (similar to E-E-A-T), re-rank using the LLM's understanding of relevance, and extract specific passages to cite. The critical insight is that different platforms have dramatically different source preferences - only 11% of websites get cited by both ChatGPT and Perplexity.

📊 Platform-by-Platform Source Preferences

This is where GEO gets complicated. Each AI engine has its own index, its own ranking biases, and its own citation patterns. Let me break down what we know:

ChatGPT Search

ChatGPT uses Bing's index as its primary retrieval backbone. Citation pattern analysis shows that Wikipedia accounts for 47.9% of citations on factual queries. It relies heavily on well-established, editorially reviewed sources. Domain authority matters here - ChatGPT is more likely to cite .gov, .edu, and long-standing publishing brands.

You can track your brand's presence across ChatGPT using dedicated tracking tools.

Perplexity

Perplexity maintains its own proprietary index of over 200 billion URLs. It averages 5.28 citations per response, and approximately 60% of its top-10 cited sources overlap with Google's organic top-10. But here's the data point that surprises most people: Reddit accounts for 46.7% of Perplexity's top-10 citations in certain industries. Reddit appears as a cited source in 7 out of 9 industry categories studied.

Google AI Overviews

Google AI Overviews use Google's own web index - the same one powering organic search. The selection process works roughly like this: E-E-A-T-based quality filtering narrows the field to approximately 30-50 trusted sources, which are then re-ranked by the LLM for contextual relevance, resulting in 15-25 sources that the model might draw from for a given response.

⚠️ The 11% Overlap Problem

Here's a stat I keep coming back to: only 11% of websites get cited by both ChatGPT and Perplexity. That's shockingly low overlap for two platforms supposedly answering the same types of questions.

What this means in practice is that a "GEO strategy" optimized for one platform may be invisible on another. If you're only tracking whether ChatGPT mentions you, you might be completely absent from Perplexity - and vice versa.

This is why I believe GEO is fundamentally a multi-platform discipline. You need presence across your own website, Reddit, LinkedIn, YouTube, review platforms (G2, Capterra), and industry publications. Different AI engines pull from different parts of this ecosystem. [EXPERIMENT CANDIDATE]

Monitoring your visibility across all platforms requires specialized AI search visibility tracking tools.

🔑 What Makes Content "Extractable"

Going back to the SEMQA research from Google (Schuster et al., NAACL 2024), AI systems construct answers by combining verbatim quoted spans with AI-generated connector text.[tryprofound]​

This means the AI is literally scanning your content for sentences it can lift word-for-word. The sentences it chooses tend to share these characteristics:

• Self-contained meaning: The sentence makes sense without surrounding context
• Specific and factual: Contains a concrete claim, number, or definition
• Clearly attributed: States who said what or where data comes from
• Well-structured: Clean syntax, active voice, no ambiguity

If your content is filled with vague, context-dependent prose - "As mentioned earlier, this approach works well for the reasons we discussed" - the AI has nothing to extract. It moves on to a competitor's page that gives it clean, quotable content.

What GEO Metrics Should You Track? [toc=GEO Metrics] [IMPROVED]

GEO success is measured through AI share of voice (how frequently your brand appears in AI-generated answers compared to competitors), citation frequency across platforms, and AI referral traffic in your analytics. The foundational GEO research also introduced two formal metrics - Position-Adjusted Word Count and Subjective Impression - that quantify how prominently and favorably your content is represented in AI responses.

🔑 Where to Start: The Practical Priority Ladder

I'll be honest: GEO measurement is still immature. There is no equivalent of Google Search Console for AI engines. So here's my priority ladder for teams just getting started:

Priority 1 - AI Referral Traffic (start today)

This is now trackable in GA4. AI platforms show up as referral sources - look for traffic from chat.openai.com, perplexity.ai, gemini.google.com, and similar domains. Many analytics platforms have added AI referral dashboards.

At MaximusLabs, we track AI referral traffic as its own channel alongside organic, paid, and social. The conversion rate differences are stark enough to justify dedicated measurement. [INSERT MAXIMUS DATA]

Priority 2 - AI Share of Voice (set up within first month)

This is the GEO equivalent of SEO's keyword rankings. It measures how frequently your brand appears as a cited source across a set of target queries, compared to competitors.

As Ethan Smith (Graphite CEO) puts it: the right metric is "share of voice - how frequently am I showing up" compared to the competition across thousands of question variants.

Several platforms now offer this tracking. The technology is still maturing - I'd pick the most affordable GEO tool and focus on establishing a baseline. For a complete guide, see our breakdown of GEO measurement and metrics.

Priority 3 - Citation Frequency (ongoing monitoring)

Track how many times (and in what context) your brand or URLs appear across AI platforms for your target queries. This requires either manual prompt testing or automated monitoring tools.

The key is consistency. Citations in AI answers are volatile - Semrush data shows that 40-60% of cited sources change month to month. You need ongoing monitoring, not a one-time audit.

📊 The Academic Metrics (for the technically curious)

Aggarwal et al. defined two metrics that are worth understanding even if you never calculate them manually:

Position-Adjusted Word Count measures how much of an AI's response references your content, weighted by where in the response your citation appears. A citation in the first paragraph counts more than one buried at the bottom - just like how position #1 in Google gets more clicks than position #10. The weighting uses an exponential decay function, reflecting how attention drops off as users read further down a response.

Subjective Impression goes deeper. It evaluates seven dimensions: relevance of your cited material to the query, influence your citation has on the overall answer, uniqueness of your contribution, prominence of position, likelihood the user clicks your citation, and diversity of the material you provide.

These metrics matter because they give us a language for what "good GEO performance" actually looks like - beyond just "were we mentioned?"

📖 Deep Dive: Essential GEO terminology including detailed metric definitions → /ai-search-101/geo/fundamentals/geo-terminology/ [link pending]

How Did GEO Evolve? A Brief History [toc=GEO History] [IMPROVED]

GEO emerged from the collision of two forces: the rapid consumer adoption of AI chat interfaces starting in late 2022, and the realization by marketing professionals that traditional SEO was insufficient for visibility in AI-generated responses. The discipline formalized between 2023 and 2024 through both commercial innovation and academic research, and has since evolved into a recognized marketing specialization.

💡 Why This Timeline Matters

I'm including this history for a specific reason. If you understand when GEO emerged and how fast it moved, you'll understand why the early-mover advantage is real and shrinking.

The Algaba et al. research (NAACL 2025) on citation compounding means that brands who established GEO authority in 2024-2025 are already building structural advantages that late adopters will struggle to overcome. History tells us the window to act is now.[mangools]​

⏰ The Timeline

Late 2022: The Spark. ChatGPT launched publicly in November 2022. Within months, it became the fastest-growing consumer application in history. Marketing professionals noticed almost immediately that AI interfaces were changing how users discovered products and information.

May 2023: First Commercial GEO Service. First Page Sage, led by CEO Evan Bailyn, announced the first commercial GEO service offering - initially called "Generative AI Optimization" (GAO). This coincided with the first large-scale empirical study of ChatGPT's recommendation algorithms.[geneo]​

November 2023: The Academic Foundation. Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, and Deshpande published "GEO: Generative Engine Optimization" on arXiv. This paper formalized GEO as an academic discipline, introduced GEO-bench, and demonstrated the 40% improvement finding.

August 2024: KDD Acceptance. The Aggarwal et al. paper was accepted at ACM SIGKDD 2024, one of the premier computer science conferences. This peer-reviewed validation moved GEO from "interesting idea" to "academically rigorous discipline."

2024-2025: Patents and Platform Evolution. Google published Patent US20240289407A1 ("Search with Stateful Chat"), revealing the architecture behind AI Mode. Google's Information Gain patent was granted. Tools for tracking AI visibility emerged - over 50 by late 2025.​[openreview]​

2025-2026: Mainstream Adoption. GEO transitioned from early-adopter novelty to mainstream marketing practice. AI referral traffic surged 357% year-over-year. Google published official guidance on succeeding in AI search features. The discipline now has established best practices, specialized tools, and a growing body of research.[hostinger]​

🔑 The 1990s SEO Parallel

There's a parallel here to the early days of SEO. When Google launched, a handful of practitioners understood that search engines would change marketing forever. Most businesses ignored them for years. The ones who moved early built durable advantages that compounded over a decade.

I think we're at that same inflection point with GEO. The research backs it up. The question isn't whether GEO will matter. It's whether you'll be early enough to capture the compounding advantage. For our thinking on where this goes next, see our analysis of future trends in GEO.

📖 Deep Dive: The complete timeline from ChatGPT launch to present-day GEO → /ai-search-101/geo/fundamentals/geo-history-evolution/ [link pending]

Key GEO Terms Every Marketer Needs to Know [toc=GEO Glossary] [IMPROVED]

GEO introduces a new vocabulary that blends AI/ML terminology with marketing concepts. The terms below are the ones you'll encounter repeatedly as you build and execute a GEO strategy. I've organized them by how often they come up in practice, not in academic order.

🔑 Terms You'll Use Every Week

Retrieval-Augmented Generation (RAG): The process where AI retrieves web documents and uses them to generate informed responses. This is the engine behind every AI answer. If your content isn't structured for retrieval AND extraction, it won't show up. This single concept explains most of GEO.[getpassionfruit]​

Semantic Chunking: Structuring content so each paragraph is self-contained and extractable in isolation. AI pulls individual passages. If a paragraph needs the paragraph above it to make sense, it won't be cited. This is the most underrated GEO skill.

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness): Google's content quality framework. AI engines use these signals to filter sources before the LLM even sees them. E-E-A-T is the entry gate to GEO visibility. We cover this in depth in our E-E-A-T for AEO guide.[blog.hubspot]​

AI Share of Voice: How frequently your brand appears in AI answers compared to competitors across target queries. This is the primary operational metric for GEO campaigns - the equivalent of keyword rankings in SEO.

⚙️ Terms You'll Need for Strategy Discussions

Query Fan-Out: When an AI breaks one user question into multiple sub-queries run simultaneously. Your content must be semantically rich enough to match reformulated queries, not just the literal search term. This is why GEO topic clusters matter.

Entity Clarity: Ensuring your brand, product, and key concepts are unambiguously defined and consistently referenced. AI models need to clearly identify who and what to cite. Ambiguity means you get skipped. Building knowledge graphs is one way to strengthen entity clarity.

Citation Frequency: Raw count of how often your URL or brand appears across AI platforms for monitored queries. The simplest GEO metric. Track weekly; expect 40-60% volatility month to month.

📊 Terms From the Research

GEO-bench: A benchmark of 10,000+ diverse queries created by Aggarwal et al. for testing GEO methods. Covers 25 domains and 9 query types. The standard test bed for GEO research.

Position-Adjusted Word Count: Measures how much of an AI response references your content, weighted by citation position. Higher weight for earlier citations - mirrors click-through rate decay.

Subjective Impression: A multi-dimensional metric evaluating relevance, influence, uniqueness, click likelihood, and diversity of citations. Goes beyond just "were you cited?" to "how prominently and favorably were you cited?"

🔗 Related Disciplines

SEO (Search Engine Optimization): Ranking in traditional search results. The foundation GEO builds on. You need strong SEO to be retrieved by AI systems.

AEO (Answer Engine Optimization): Winning featured snippets, PAA, voice answers. A subset that GEO extends. AEO targets answer boxes; GEO targets AI-synthesized responses. See our full AEO guide.

LLM (Large Language Model): The AI that generates responses (GPT, Claude, Gemini). The "engine" in generative engine. Understanding LLM behavior is core to GEO strategy.

📖 Deep Dive: The comprehensive GEO glossary with expanded definitions → /ai-search-101/geo/fundamentals/geo-terminology/ [link pending]

What I'm Thinking About Next

GEO is moving fast - faster than any marketing discipline I've worked with. And there are some things I don't have answers to yet.

I'm particularly curious about what happens when most content on the internet is GEO-optimized. The Aggarwal et al. study showed that lower-ranked sites benefit most from GEO today. But what happens when everyone is doing it? My hypothesis is that differentiation will shift from format optimization (citations, statistics) to information uniqueness - original research, proprietary data, genuine first-person expertise. Google's Information Gain patent  points in exactly this direction.[openreview]​

I also think we'll see platform-specific GEO strategies emerge as a distinct sub-discipline. The 11% citation overlap between ChatGPT and Perplexity tells me these platforms will diverge further, not converge. Optimizing for all of them simultaneously will become harder, not easier.

And then there's the question that keeps me up at night: what happens to the creator economy? The Princeton researchers who wrote the GEO paper explicitly flagged this concern. If AI engines synthesize and cite content but dramatically reduce clicks to the original source, we need new economic models. I don't have the answer, but I think every GEO practitioner should be thinking about it.

If you're just getting started with GEO, explore the spoke articles in this series. Each one goes deep into a specific aspect of what we've covered here. And if you want to see how these principles translate to execution, start with the AEO implementation checklist - it's the most actionable starting point I can offer.

References

Research Papers

Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. "GEO: Generative Engine Optimization" ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024.​

Schuster, T., Lelkes, A., Sun, J., Gupta, D., Berant, J., Cohen, W.W., & Metzler, D. "SEMQA: Semi-Extractive Multi-Source Question Answering" NAACL, 2024.

Algaba, A., Mazijn, C., Holst, J., Tori, F., Wenmackers, S., & Ginis, V. "Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias" NAACL Findings, 2025.

Li, W., Stenzel, R., Eickhoff, C., & Bahrainian, S. "Enhancing Retrieval-Augmented Generation: A Study of Best Practices." COLING, 2025. [URL NEEDED]​

Gao, Y., et al. "Retrieval-Augmented Generation for Large Language Models: A Survey." arXiv, 2023.​

Patents

Patent US20240289407A1. Rofouei, M., Shukla, A., Wei, Q., Tang, C., Brown, R., & Piqueras, E. "Search with Stateful Chat." Assignee: Google LLC. Filed: February 27, 2024.​​

Google. "Contextual Estimation of Link Information Gain." Filed: 2018. Granted: June 2024.

Patent US20180157747A1. Tiwary, S.K., Rosenberg, M., Gao, J., Song, X., Majumder, R., & Deng, L. "Systems and Methods for Automated Query Answer Generation." Assignee: Microsoft Technology Licensing LLC. Filed: December 2, 2016.​

Technical Documentation

Google Search Central. "Top ways to ensure your content performs well in Google's AI features" Published: May 2025.

Reid, E. "AI in Search: Going beyond information to intelligence Google Blog, May 2025.

Google Search Central. "Our latest update to the quality rater guidelines: E-A-T gets an extra E." Published: December 2022.​

Secondary Sources

EZ Newswire. "A History of Generative Engine Optimization (GEO)." Published: November 5, 2025. [Secondary source]

InRhythm. "Executive Interview Series: Evan Bailyn on The New Marketing Frontier of GEO" Published: July 2025. [Secondary source]