A beginner’s guide to Generative Engine Optimization (GEO) 

Generative Engine Optimization (GEO), also known as AI Search Optimization (ASO), Conversational Search Optimization (CSO) or even Large Language Model Optimization (LLMO) is a burgeoning discipline of digital marketing, driven by the rise of large language models like ChatGPT, Claude and Perplexity.  

Like traditional SEO (Search Engine Optimization), which focuses on enhancing visibility in search engines, GEO focuses on enhancing visibility in generative large language models. As LLMs become ever more prevalent in our day-to-day lives, GEO represents a shift in SEO, away from the search engines of the past and towards the ones of the future.  

In this guide, we will break down what GEO is, why it matters, what challenges it presents and how businesses can use GEO now to get ahead of the competition.  

What is GEO? 

In simple terms, GEO is the process of adapting your digital content so that AI platforms can understand, extract, and cite it when answering user queries. The easier it is for these platforms to understand what you do, the easier it is for them to recommend you to users. The ultimate goal of GEO is to ensure that when someone asks an AI a question related to your specialty, the AI’s answer references your brand. 

Why is GEO important and what are the benefits? 

Generative AI tools are increasingly being used for the functions that search engines once dominated. A study by TechRadar found that 27% of U.S. consumers are using AI chatbots instead of traditional search engines, and this might just be the beginning. Gartner predicts that by 2026, 25% of user searches will be conducted through generative AI tools rather than traditional search engines. 

These models are growing in popularity because users enjoy asking natural language questions and receiving natural language responses. These tools provide a faster and more convenient way to search, allowing users to edit and adapt their search iteratively with a model, rather than clicking through multiple web pages to find an answer. Even when Google can provide a website that answers a query, this website’s text is set, it can’t be summarized, explained, or questioned further in the way that AI models can. As AI tools become embedded in everything from voice assistants to business software, users will, whether deliberately or not, be relying on generative AI search for answers. In all cases, if your content isn’t tuned to be picked up by these models, it risks becoming invisible. 

Companies that invest early in GEO are positioning themselves to maintain visibility and authority as this new search paradigm scales up. In short, GEO is becoming as important in the 2020s as SEO was in the 2000s; it’s the next evolution of search marketing. 

Top five ways for businesses to begin with Generative AI Optimization 

1. Answer the right questions, in the right way 

Start by identifying the questions your audience is asking and take note of how they’re phrasing them. Generative AI draws citations from natural, conversational language, so your content should reflect that. 

• Platforms such as AnswerThePublic, Reddit, and Quora can be used to identify real-world queries in natural language. 

• Talk to your sales and customer service teams; they hear user concerns in plain language every day. 

• Consider on-site surveys or semantic analysis of client emails to capture the tone and vocabulary that users are employing when asking questions about your business 

Tip: Write like a human, not a brand. This increases the chances of AI using and citing your content in its answers. 

2. Use schema markup to give AI clear context 

Structured data isn’t just for traditional SEO anymore; it’s now essential for GEO. Structured data gives web crawlers explicit context regarding the nature of your content. While large language models don’t crawl the web themselves, structured data helps search enabled AI tools (like Google SGE or Bing Chat) understand, index and cite your content more effectively. 

For example, marking up FAQs, how tos, and product information aids in signalling to AI exactly what question this content answers, giving it the confidence to provide your content in its results. 

• Implement schema on FAQs, how-to pages, product details, and organization information. 

• Clear metadata (titles, meta descriptions, H1-H3 tags) gives models further content to help understand and surface your content. 

Why it matters: Schema gives generative tools the confidence to quote you accurately, because they know exactly what your content is saying. 

3. Write like a friend, not a brochure 

AI responds better to simple, helpful language over marketing fluff or technical jargon. People talk to AI models in the same way they talk to a knowledgeable friend. Therefore, these models are more likely to pull information from sources that match this tone. Focus on conversational tones and explanations that could be understood by users with no prior knowledge of your industry or offerings.  

• Avoid complex phrases such as “leveraging cutting-edge methodologies to maximize operational efficiency”. Instead, opt for simpler phrasing such as “helps teams save time”. 

• Think: How would I explain this to a friend over coffee? 

Remember: If your content sounds too complex or robotic, AI will likely pull from an easier-to-understand source. 

4. Spread your content beyond your website 

Don’t just publish and hope – remember to repurpose and share across the platforms AI models learn from. Large language models train on natural language data from the internet, thus, expanding the reach of your content onto the internet’s largest repositories of natural speech can increase your influence on the AI’s knowledge, improving the chance of being cited. 

• Turn blog posts into Reddit answers, Quora responses, or LinkedIn summaries. 

• Engage in discussions on niche forums and social media where natural language is abundant. 

• This increases the likelihood of your insights being picked up and cited by generative tools. 

Overall:  AI tools draw from public content across the internet. Share your expertise in the places where real conversations happen. 

5. Track, test, and evolve your GEO strategy 

Just as SEO involves tracking your search rankings and adjusting strategy, GEO involves monitoring how and where your content appears in AI outputs and refining accordingly. This can be done in four steps. 

• 1. Identify your target queries. What searches do you want to show up for? 

• 2. Run regular tests: Ask key LLMs your target queries. Does your brand appear? 

• 3. Analyze why competitors might be winning. Do they have clearer, more topical content? 

• 4. Action your insights. How can you beat your competition at their own game? 

Bottom line: Stay curious, monitor results, and refine your content continuously to stay ahead. Also consider new GEO-specific tools like PromptWatch, Otterly, or HubSpot’s GEO features. 

The challenges of Generative Engine Optimization 

One obvious challenge with GEO is that if an AI provides the answer to a user’s question, the user might not ever visit your site. In the generative search experience, just because an AI references your content, it doesn’t necessarily mean a user will click the link. However, that visibility is still valuable; if you’re not present in AI search results, the user may never know you exist. Especially in B2B scenarios, the ultimate goal for both you and the right prospect is often a phone call or a meeting – something AI can’t do (at least for now). 

With traditional SEO, Google dominates the market. As such, one could optimize for Google exclusively and see great results. In the AI space, this dominance isn’t so clear, with platforms such as ChatGPT, Bing, Bard, Claude, Perplexity and Gemini all vying for the top spot, a clear leader hasn’t been crowned. What’s more, each of these models has different data sources and collection methods. Some, such as ChatGPT, primarily rely on static training data, but when browsing is enabled, access the web via Bing to enhance responses. Others, like Perplexity, retrieve information in real time by default, while hybrids such as Copilot combine both approaches. Optimizing for one might not work for another. Given how these algorithms and models update so frequently, it’s a challenge for teams to keep up and understand how each platform finds and understands your content; as such, GEO best practices may evolve continuously, much like SEO did in the 90s and 2000s, but faster. 

In SEO, the tools we have to monitor rankings have been around for a while. For GEO, measuring impact is less straightforward. While some AI search experiences provide citations with links, allowing you to see and measure referral traffic, others don’t. The lack of analytics and reporting for AI-driven visibility is a challenge. Despite still being in their relative infancy, there are now a number of GEO tools available, such as PromptWatch, Rankscale, and Otterly. These tools can identify where and how brands are referenced in AI content, simulate user queries to uncover discovery paths and keyword trends, gauge brand sentiment across large language models, and measure how frequently brands appear in AI search results.  

While new, these tools can provide information to generate actionable GEO insights – and their existence highlights GEO as a growing discipline. At Aspectus, we can track emerging GEO signals for clients, identifying mentions, measuring search visibility within AI platforms, and surfacing insights to inform GEO strategy. 

Optimizing for generative engines often means creating content in new formats and with different structures. Best practices include providing succinct answers, FAQs, structured data, and multimedia content. Producing and maintaining this diverse, well-structured content can be resource intensive. Businesses may find it challenging to invest in GEO-specific content creation on top of regular marketing content. There is also a learning curve as your team may need training on how AI models work and what “AI-friendly” content looks like. 

Another important consideration is accuracy. Generative AI tools can “hallucinate” – generating incorrect information or attributing statements to the wrong source. Just because your brand is mentioned doesn’t always mean it’s based on real or correctly interpreted content. This makes it even more important to ensure your content is clear, well-structured, and widely distributed. Since LLMs learn from the data they’re trained on, content that’s inconsistent, ambiguous, or hard to find increases the risk of hallucinations or incorrect associations. Clear and accessible content helps AI form more accurate semantic links and improves performance in retrieval-augmented systems, which rely on high-quality source material to ground their responses in fact. 

In summary 

As generative AI reshapes the way people search for and engage with information, GEO is emerging as the natural next step in digital marketing strategies. Much like the rise of SEO in the early 2000s, the businesses that adapt early will be the ones to gain lasting visibility and authority in this new environment.  

While challenges remain, from tracking performance to adapting content across multiple AI platforms, the potential rewards make GEO a vital consideration for forward-thinking marketers. By understanding how these models operate, creating content that speaks naturally to user intent, and distributing it strategically across the web, brands can position themselves to be consistently discovered in AI-generated answers. GEO isn’t a replacement for SEO, it’s an evolution. Treat it as an ongoing process, stay agile, and your brand will be well-placed to thrive in the future of search. 

Key takeaways 

What is GEO? 

Adapting content so generative AI tools can understand, cite and display your expertise in responses. 

Why should businesses invest in GEO? 

It ensures visibility in AI search results, establishes authority, and future-proofs content beyond traditional SEO. 

How can I start implementing GEO now? 

Identify real conversational queries, apply schema markup, use a friendly tone, distribute content widely, and monitor AI citations.