What is GEO? A Beginner’s Guide to Generative Engine Optimization

Published by abraham • October 9, 2025

Online searching as we know it today faces dramatic changes in how it works. One new feature that reflects these changes is GEO. You may be asking: what is GEO? Generative Engine Optimization represents the next step in digital marketing’s evolution. By 2026, traditional search volume is expected to drop by 25%, and organic traffic could decrease by more than 50% as more people turn to AI-powered search solutions.

These changes make sense. About 79% of consumers plan to use AI-boosted search next year, as the trust in generative AI search results stands at 70%. Platforms like ChatGPT boast over 180.5 million monthly active users, while other platforms like Perplexity AI have seen an 858% surge in search volume. These platforms are reshaping the scene of online information discovery, so digital marketers must understand how GEO works alongside traditional optimization methods. Companies have quickly adapted to these changes in AI with roughly 65% now using generative AI regularly.

Generative AI now powers search, delivering combined answers directly in the results. Often, users don’t even need to visit websites. This article explores GEO search, how it differs from traditional SEO, and the strategies you can use to make your content stand out in an AI-driven world.

What is GEO and Why it Matters in 2025

Generative Engine Optimization (GEO) is changing digital marketing strategies faster as we head into 2025, with AI systems playing a crucial role in how people find information online. Marketing professionals need to understand this new field and how it may affect their business.

Definition of Generative Engine Optimization

Generative Engine Optimization (GEO) improves how digital content appears on AI tools like ChatGPT, Claude, Gemini, Perplexity, and Google’s AI Overviews. Regular SEO works to boost rankings on search engines, while GEO works to make large language models (LLMs) notice, reference, and include content in their answers to people’s questions.

GEO optimizes technical website elements that help AI engines find and read brand content. This practice goes beyond simple keyword optimization, focusing on how AI systems understand context and brand connection.

How GEO Fits Into the AI Search Landscape

GEO has become a valuable addition to traditional SEO in today’s AI search landscape. The biggest change is that visibility now means appearing in the actual answer, not just ranking high on results pages.

AI-native search now spans platforms of all types—like Instagram, Amazon, and Siri—with different models and user intentions powering each platform. Users now type longer queries (23 words on average, compared to 4 for traditional search) and spend more time (about 6 minutes) on their searches. These changes in search behavior need a fresh optimization strategy.

GEO targets two main types of AI-powered platforms:

Traditional search engines with generative components (like Google’s AI Overview)
Dedicated generative engines like ChatGPT, Gemini, and Perplexity that blend responses instead of linking

Why GEO is Gaining Traction Now

GEO has gained massive momentum in 2025—and with good reason. AI platforms are thriving: ChatGPT now has over 180.5 million monthly active users, while Perplexity AI has seen an 858% increase in search volume in the last year.

Gartner expects traditional search volume to drop 25% by 2026, while organic search traffic could decrease by more than 50% as people welcome AI-powered search. Nearly 79% of consumers plan to use AI-enhanced search next year, and 70% already trust generative AI search results.

The Princeton-led GEO study from November 2023 showed that optimizing content for generative engines can increase source visibility by up to 40% in AI responses. Simple methods like keyword stuffing didn’t work well, while adding statistics, citations, and quotations improved performance significantly.

GEO has become essential because it represents a radical change in how people find information—asking questions instead of searching. By 2028, it’s estimated that over 36 million users in the US will use generative AI as their main tool for online search.

Small and mid-sized companies can get valuable free exposure and brand recognition by appearing in AI-generated answers. This shift has created new service opportunities, as digital agencies now offer “AI readiness” audits and GEO services to help businesses adapt to this evolving digital world.

GEO vs SEO: Understanding the Key Differences

The gap between SEO and GEO represents a major shift in how content reaches audiences in 2025. AI-powered search has changed user behavior, making it crucial to understand these differences for digital visibility.

What is GEO vs SEO?

Search Engine Optimization (SEO) began 25 years ago to help websites rank higher on search engines like Google and Bing. Companies use specific keywords, quality content, and backlinks to boost their place in search results.

Generative Engine Optimization (GEO) brings a new way to tailor content to AI search engines and language models. These systems don’t just rely on keywords, they use AI to craft in-depth answers to questions. Content designed for GEO gets noticed and mentioned by advanced AI systems like ChatGPT, Claude, Gemini, Perplexity, and Google’s AI summaries.

The key difference shows in their results. SEO focuses on ranking website links to get clicks, while GEO creates direct answers that blend information from multiple sources.

How Generative Engines Process Content

Generative engines work differently from traditional search engines. They use advanced natural language processing instead of just matching keywords and analyzing backlinks.

AI systems pick content based on unique criteria, gathering and analyzing data from websites across the internet. Natural language understanding helps them assess contextual relevance rather than just counting keywords.

AI engines favor content with:

Clear citations and references from credible sources
Statistical evidence and data points
Well-structured, easily parsed information
Authoritative language and expert quotations

A Princeton-led study revealed that adding citations, expert quotes, and statistics improved source visibility by 40% in AI responses. This approach outperformed traditional SEO tactics like keyword stuffing.

Why Traditional SEO Alone is No Longer Enough

SEO remains important, but relying solely on traditional methods is no longer enough.

Search habits have changed dramatically. About 80% of consumers now depend on AI-written results for 40% of their searches. This has cut organic web traffic by 15-25%. Users get answers without visiting websites in this “zero-click” environment.

AI-powered search looks at entire topic ecosystems. It examines content depth, expertise, and how detailed the information is. Search algorithms change faster than manual optimization can keep up, while marketers need new strategies as generative AI becomes part of search experiences.

How Generative AI Engines Work

Generative AI search engines work in ways that are completely different from traditional ones. The technology behind them is complex, and understanding it plays a vital part in making your GEO strategy work.

Data Collection and Training

The backbone of generative AI lies in foundation models that rely on learning through massive datasets. These models process enormous amounts of random unorganized internet data to recognize patterns and relationships. Training such models requires tremendous computing resources, with teams often running thousands of GPUs for weeks—costing millions of dollars.

Foundation models—especially large language models (LLMs)—are the foundation of generative engines. These models get better by doing millions of “fill in the blank” exercises and tweaking their settings to make fewer mistakes. This way of learning works great with natural language because you don’t need as much hand-labeled data, which can be a real headache to collect.

These trained models can create new content from information they’ve never seen before. Many modern systems now use retrieval-augmented generation (RAG), allowing the foundation model to tap into external sources and stay up to date.

Natural Language Processing and Synthesis

Natural language processing helps AI engines spot, understand, and create text that sounds human—mixing computational linguistics with statistical modeling and deep learning. Unlike old-school search engines that just match keywords, NLP helps AI systems get the full picture from complete sentences.

The transformer architecture in these systems uses an “attention” mechanism to figure out what matters most in data sequences. This smart setup lets the model:

Look at full sentences at once, not just word by word
Understand how different parts of the data connect
Build accurate pictures of what information means

These sophisticated systems can grasp intent, meaning, and related topics when creating responses, meaning generative AI produces content that makes sense and matches what users want to say.

How AI Selects and Cites Content

AI search engines use several steps to handle user questions. First, they decode questions with advanced models like BERT and MUM to really understand what users need. Then, many use retrieval-augmented generation to check trusted sources, pull out important points, and create answers.

The core team at AI engines usually select content from trusted sources like Wikipedia, news sites, and expert blogs. Content with clear headings, lists, and simple explanations works best for AI systems.

For citations, systems like ContextCite figure out which sources matter most through “context ablations.” They remove parts of context and see how answers change to spot key information sources. Different engines handle citations their own way—Perplexity uses numbered citations in answers, Google’s AI Overviews shows source cards with previews, and Microsoft’s Bing Copilot adds footnotes that link to sources.

These technical basics help you create GEO strategies that work well with how generative engines handle, pick, and cite content.

Core Strategies to Optimize for GEO

Your content’s visibility in generative AI responses can improve with the right optimization techniques. Understanding how these systems review information helps you create targeted strategies that improve your GEO performance.

Use of Citations and Expert Quotes

AI systems prefer content with proper sourcing that proves credibility. The Princeton study shows that trusted source citations and expert quotations made content more visible in generative engine recommendations. Your content should include references from authoritative websites, academic studies, and industry experts to build trust. This method matches the experience-expertise-authoritativeness-trustworthiness (E-E-A-T) principles that AI engines value more now.

Incorporating Statistics and Structured Data

AI tools rely on numbers to build trust and compare stats with reliable sources. Any trusted stats you want to use should come from .gov and .edu sites, industry research, or peer-reviewed papers. Sharing direct links to studies helps confirm facts and keep things clear.

Writing for User Intent and Clarity

GEO differs from traditional SEO by creating content that answers user questions directly. Your audience’s specific questions need clear, direct answers. AI models favor content that is conversational yet precise, avoids jargon, and presents information in a logical order. Content becomes even more relevant when it aligns with real user questions and centers around well-defined entities.

Optimizing for AI Readability and Chunking

“Content chunking” breaks text into focused sections that AI processes easily. AI systems extract relevant information better with descriptive headings, short paragraphs, and clear structure. This method helps AI interpret your content when it creates responses to user queries. Your content has a better chance of appearing in responses because AI often slices web pages into chunks.

Leveraging Multimedia and Schema Markup

AI engines look at all content elements—not just text. Videos, images, and infographics add context and boost user participation. Schema markup serves as a bridge between your content and AI systems, making it more visible to AI search engines when companies follow schema.org standards. Markup types like FAQ, HowTo, and product info help AI models process clearly labeled information.

Research-Backed Tactics and Domain-Specific Tips

Research shows clear paths to success for anyone looking to become skilled at GEO. Studies reveal which strategies actually work in this ever-changing field.

Top-Performing GEO Methods From Studies

Research points to several GEO techniques that get results. Including citations from trusted sources can boost visibility in AI-driven searches by over 40%. Additionally, incorporating expert quotes and using statistics effectively can further increase visibility and benefit your business.

Style changes also make a difference. Improved readability and flow boosts visibility by 15-30%, while traditional SEO tricks like keyword stuffing fall flat in generative engines.

What Works Best for Different Content Types

Content categories need their own optimization approach to succeed. Technical terms and authoritative writing work exceptionally well for debates, history, and scientific topics, while better flow and readability give stronger results in business, science, and health content.

B2B companies win with detailed feature comparisons, while healthcare and other regulated industries do best when they highlight expert credentials and compliance. The way content looks matters too—well-structured pages see 30-40% better visibility in AI results.

Generative Engine Optimization marks a radical shift in how businesses connect with their online audience. AI-powered search is changing user behavior fast, and traditional SEO tactics aren’t enough anymore. The numbers tell a compelling story—traditional search volume will drop 25% by 2026, and 79% of consumers will use AI-enhanced search within a year.

The digital world now pushes for a shift in how AI systems handle information. Content creators need to focus on building clear organization, relying on trusted sources, using accurate facts, and including expert opinions. Concentrating on keyword stuffing won’t do much. Companies that master these strategies see impressive results, boosting conversion rates and generating high-quality leads directly through AI platforms.

The gap between successful and unsuccessful digital marketing continues to grow wider. Organizations that understand both traditional SEO and new GEO principles have a strong edge as traffic patterns evolve. Without a doubt, businesses must prepare for AI’s growing role in how people find information.

GEO brings together strong content, solid technical setup, and credibility. Early movers in this space will succeed while others may struggle to maintain visibility. The marketers who succeed tomorrow will be the ones who adjust fast and rely on proven strategies built around how AI engines evaluate, select, and link to content online.