As generative AI tools like ChatGPT, Gemini, Deepseek, Perplexity, and others redefine how users search for information, a new field has emerged: Generative Engine Optimisation (GEO). Unlike traditional search engines that return a list of links, these platforms provide direct, conversational responses. GEO is the practice of optimising your content to be cited, referenced, or even quoted by these Large Language Models (LLMs), whether through public interfaces or API integrations.
In this article, we’ll explore what GEO is and how it compares to traditional SEO. We’ll start by examining the similarities between GEO and SEO, before diving into what sets GEO apart. You'll learn about the key factors that influence how content is surfaced in generative engine responses and how brands can integrate SEO and GEO strategies for maximum visibility. Finally, we’ll discuss what to expect from the future of SEO in a world increasingly shaped by generative AI.
The Similarities Between GEO and SEO
While Generative Engine Optimisation (GEO) might sound like a departure from traditional SEO, the two actually share more in common than you might expect. Both disciplines aim to increase online visibility and ensure your content is easily discoverable, whether by search engines like Google or AI models like ChatGPT and Claude. In fact, Google's own Search Generative Experience (SGE), now branded as AI Overviews, shows just how intertwined these approaches are becoming.
At the core of both GEO and SEO lies the importance of content quality. Clear, relevant, and well-structured content continues to be essential. Just as SEO relies on keyword strategy, headings, metadata, and schema markup to help Google understand your site, GEO similarly depends on structured, digestible content chunks that LLMs can reference and surface in natural language responses.
Technical performance also matters in both. Fast loading times, mobile-friendly design, and clean site architecture all contribute to better visibility, whether in search engine results pages or generative answers. Moreover, both strategies are driven by user intent. Understanding what your audience is looking for and tailoring content to meet that need remains crucial, whether you're optimising for traditional search or conversational AI.
Ultimately, GEO and SEO aren’t competing approaches; they’re parallel tracks in a rapidly evolving search landscape. When integrated thoughtfully, they strengthen one another, ensuring your content reaches the widest and most relevant audience across both search engines and generative platforms.
GEO vs. SEO: What’s the Difference?
In traditional SEO, comprehensive and scannable content plays a key role. Search engines have historically rewarded pages that are both in-depth and easy to understand. But with Generative Engine Optimisation (GEO), this is starting to shift.
For generative AIs, it’s not necessarily about having the most exhaustive content. Instead, what's most valuable are concise, high-impact chunks of information that can be easily referenced in AI-generated responses.
This means content needs to be not only well-structured and informative but also clear, readable, and quotable, focusing on short, digestible sentences that are easy for language models to extract and use.
The differences go further. GEO is built around how large language models (LLMs) operate, they don’t search the web in real time like Google, but instead generate answers based on statistical patterns learned from massive training data. These models don’t “know” facts, they predict likely sequences of words. That’s why traditional SEO elements like backlinks are losing strength in this context. Instead of links, LLMs value brand mentions and the overall way a brand is semantically positioned online. For instance, being consistently described as “a trusted B2B agency” across authoritative sources has more impact than accumulating backlinks alone.
GEO also differs in the way it handles complex or long-tail queries. While traditional search often returns a list of links based on broader relevance, generative engines are capable of synthesising detailed answers that directly address niche or specific questions. This shift challenges how we measure visibility, no longer by SERP ranking alone, but by the likelihood of being cited in AI-generated content. As a result, success in GEO depends not just on being found, but on being understood, trusted, and contextually relevant to both users and the AI engines they increasingly rely on.
