Generative Engine Optimization

By Pieti Kinnunen

February 4th, 2026

Internet search behavior is changing. Instead of scrolling through a list of links in the search results of Google (or other search engines), users are increasingly getting answers directly from AI-powered tools like ChatGPT, Google’s AI Overviews, Perplexity, and other generative search engines. This shift has given rise to a new discipline: Generative Engine Optimization (GEO).

What is GEO?

The term GEO was coined in a 2024 research paper by Pranjal Aggarwal et al titled “GEO: Generative Engine Optimization”. They formulated search engines that were augmented with generative engines and then went about optimizing pages for those engines. GEO is like SEO for traditional search engines but for generative engines. GEO focuses on optimizing a website's content so it can be discovered, understood, and included by generative AI systems in their responses and overviews. Traditional SEO is designed around ranking web pages in search engine results while GEO is about becoming a trusted source for AI-generated responses.

Key differences

Key differences between SEO and GEO come down to who the content is designed for, what success looks like, and how the content is structured. Traditional SEO targets search engine algorithms alongside human readers, with the goal of ranking well in search results. GEO, by contrast, targets large language models (LLMs) that analyze, summarize, and synthesize information to generate direct conversational or summarized answers for users.

The desired outcome also differs between the two approaches. SEO primarily aims to get people to click on a search result and improve rankings in search engine results pages. GEO focuses on being cited or referenced within AI-generated responses, where visibility may occur even without a direct website visit. The traditional statistics (for example click through rates or ranking in results) that determine the effectiveness of SEO don't work for GEO. Generative engines use information from multiple sources in a single response. Factors such as length, uniqueness, and presentation of the cited website determine the visibility of a website and the effectiveness of GEO.

Content structure should reflect these different goals. SEO has historically emphasized keyword optimization and backlinks among the most important ranking signals. GEO places greater importance on clarity, factual accuracy, structured data, and semantic relevance, ensuring that content could be easily understood and confidently reused by generative systems.

Concepts

At its core, Generative Engine Optimization is about making content easy for AI systems to understand, trust, and reuse. This starts with writing clearly and directly and focusing on fully explaining a topic rather than targeting individual keywords. Content that defines concepts, answers common questions, and avoids unnecessary complexity is thought to be more likely to appear in generative engine responses.

Structure plays an important role as well. Pages that use clear headings, logical flow, and concise sections are easier for generative engines to parse and summarize. Strong organization improves both machine comprehension and human readability, which remain closely aligned. Keeping your site readable and interesting to humans is also still important.

GEO also depends on demonstrating real expertise on a subject. Generative engines tend to favor sources that are credible, accurate, and authoritative. Publishing in-depth, up-to-date content and grounding explanations in real world experience helps establish that trust. In the original GEO research paper, it was found that citing sources, including quotations from relevant sources and statistics can boost source visibility in generative responses.

Finally, GEO builds on solid technical foundations. Clean HTML, proper metadata, and overall site performance all contribute to how effectively generative systems can interpret and reference your content. So having a performant website is still important in the era of generative engines and LLMs.

How Generative Engines Choose Sources

Generative AI systems don't rank pages in the same way a traditional search engine usually does. Instead, they evaluate sources based on factors such as:

• Clear explanations of concepts

• Well-structured content

• Consistent terminology

• Alignment with commonly accepted facts

• Topical authority and expertise

• Machine-readable signals (schema, metadata, clean HTML)

These factors are of course not really visible to us as users or website owners and they are largely decided and controlled by the companies building the LLMs. Different generative AIs respond better to different things and all of them are black boxes for regular users so you can’t really know for sure what works best for each. Though in general if your content is ambiguous, poorly written and structured or overly promotional, it is probably less likely to be used by a generative engine.

Final Thoughts

Generative Engine Optimization is not a replacement for SEO, but an extension of it. Strong SEO fundamentals make GEO easier and good GEO practices make your SEO content more future-proof. Together they help ensure your content is discoverable whether users are searching on the internet or prompting an LLM.
Searching assisted by generative engines is still evolving, but the direction seems clear: content needs to be understandable not just to people, but also to the AI systems that summarize the web. By focusing on clarity, structure, technical quality, and real expertise, you can start adapting to this shift and stay more visible in a world where answers are increasingly generated, not just listed in search results.