What is Generative Engine Optimization (GEO)? & How to Do It

Key GEO Facts (Quick Summary)

• GEO = optimization for AI answers, not just search engines.
• LLMs use training data & live web data (e.g., Bing, Google).
• Most cited sources: Reddit, YouTube, newswires, Wikipedia, etc.
• GEO prioritizes authority, clarity, recency, and narrative consistency.

GEO helps your brand to appear in AI platforms like ChatGPT and Google Search Summaries — but how does it work? This guide has everything you need to know to get started with GEO.

As generative AI reshapes how people research, learn, and make decisions, a new discipline is emerging: Generative Engine Optimization (GEO). Just as SEO helped brands get discovered on Google, GEO ensures they appear accurately and positively within AI-powered platforms like ChatGPT, Gemini, and Copilot. With millions of consumers now turning to generative engines instead of search engines, GEO is fast becoming essential for marketing and PR professionals looking to influence how their brand shows up in this new discovery layer, where conversations, not clicks, determine visibility.

Contents

• What is Generative Engine Optimization (GEO)?

• Why Should Marketing & Insights Teams Care About GEO?

• How Does GEO Work? Key Mechanisms & Signals

• How Can You Get Started With GEO?

• 6 Common GEO Mistakes to Avoid

• What's the Difference Between GEO and SEO?

• Increase Your GEO Readiness with Meltwater

• Generative Engine Optimization FAQ

What is Generative Engine Optimization (GEO)?

Generative Engine Optimization (GEO) is the practice of ensuring that AI platforms such as ChatGPT, Google Gemini, Llama, etc., provide accurate, positive, and up-to-date information about your brand when users ask questions. These platforms are referred to as Generative Engines, Answer Engines, or Large Language Models (LLMs). They are increasingly used by consumers to research purchases and make decisions.

GEO is a way of making sure that AI platforms give people the right information about a company’s brand and products. 

For example, if a person asks ChatGPT: “What is a good family car for camping trips?” then the auto manufacturers would want ChatGPT to suggest their SUV models, and list the benefits. To make sure that happens, the auto brands need to use GEO.

GEO ensures your brand shows up correctly within these AI-generated responses.

The practice of GEO involves several steps, but it is mostly focused on creating, structuring, and publishing content in the best way to influence the responses given by AI Generative Engines. 

In this article we’ll go into the detail of how GEO works, why it’s important, and how you can use it to make sure your brand is featured positively in AI platforms. 

Why Should Marketing & Insights Teams Care About GEO?

For a long time marketers have been able to rely on the fact that consumers almost exclusively use Google to research buying decisions. So they have been able to use Search Engine Optimization (SEO) to ensure their brand’s website appears in the organic search results, or use paid search advertising to buy ads on relevant search results. 

This is a highly effective way of targeting the right audience at the right time. But now things are changing. 

Changing user behavior: AI + conversational search

Increasingly, consumers are using tools like ChatGPT to research their buying decisions. Data from Meltwater’s 2026 Global Digital Report shows that ChatGPT.com is now the fifth most visited website on the web.

While search engines remain popular, many search queries now produce an AI generated summary that often provide enough information to divert users away from clicking through to websites, because their question has already been answered. McKinsey reports that around half of all Google search queries already produce AI generated summaries, and that number is increasing all the time. 

With conventional web search, consumers are performing research manually, trying to find trustworthy sources and sifting through information to reach the answers they seek. But with Generative Engines, the AI is performing the research automatically, using its existing base knowledge alongside supplemental information pulled from more recent online sources. A search engine shows users where to find answers, while a Generative Engine just gives them the answer. 

For marketers, the key takeaway is that few top-of-funnel consumers are visiting your website, because during the awareness phase their information needs are being met by Generative Engines. This makes it more difficult to move them down into the consideration phase. 

TIP: For more data around growing AI usage and online consumer behavior, download our 2026 Global Digital Report, featuring nearly 700 pages statistics to help inform your strategies next year.

Risks of ignoring GEO

You already know that everybody is talking about AI, and all of your competitors are having the same discussions as you about what it means for their marketing strategy. Those who move decisively to implement GEO, ensuring their brands are represented clearly and positively in Generative Engines, will capture the largest share of those consumers who start their buying journey with an AI query. 

Those who ignore GEO will lose ground, as growing numbers of consumers turn from search engines to Generative Engines, the size of the audiences that can be reached through conventional search marketing will decrease. 

How Does GEO Work? Key Mechanisms & Signals

How do generative engines (LLMs) surface content?

It’s important to understand that while Generative Engines are based on similar technology, they all work a little differently. Broadly speaking they all use two different sources of knowledge to provide an answer to a query: 

Training Data: All LLMs begin by being trained on a huge volume of existing content and data — we’re talking about dozens of terabytes (a terabyte is 1,000Gb). This training data comes from a wide variety of sources (including your website), and gives the LLM its base knowledge about the world. The LLM does not have any knowledge of anything that happened after its initial training. 

Live Web Search: To overcome the knowledge cut-off problem, the training data is supplemented with fresh information gathered from the internet. If the Generative Engine thinks that a user’s query would benefit from more up to date information than it already has, it fetches new content from trusted online sources and works that into its response. Google uses its own search engine to find fresh information, while ChatGPT uses various sources, including Microsoft Bing.

It’s also important to note that AI does not quote any of this content verbatim in its responses. Much like a human, it uses the information it has learned to inform its own answer, and that can include information from various sources, including the press, competitors, consumer reviews. So don’t expect it to always be on-message!

Another key point is that unlike a search engine, an LLM does not have an index of content which it searches to find answers. It would be impossible to search ChatGPT for all mentions of your brand — that’s simply not how it works. 

An LLM is more similar to a human brain, in that it has a lot of knowledge stored in patterns and relationships between words and concepts. It is extremely difficult, verging on impossible, to examine and understand the detail of an LLM’s learned knowledge because of the sheer scale and complexity; this is a challenge referred to as Black Box AI. 

But we do understand how Generative Engines work at a high level, and we can analyze the kind of responses they produce for relevant queries, which gives us insight into how we should optimize our content for increased visibility.

What content features improve GEO-visibility?

Bear in mind that when you’re optimizing content for Generative Engines, the goal is to write copy that is easier for AI to digest and incorporate into its responses.

• Clarity: Avoid overly elaborate language. Do not use corporate-jargon. Keep it simple, professional, and to the point. Leave no room for ambiguity. 
• Authority: Wherever possible back up your statements with data from reputable sources, quotes from experts or industry leaders, and reference-able proof points. 
• Structure: Organize your pages well with clear titles, headings, and subheadings. Use heading tags (H1, H2, etc) correctly to specify the hierarchy of your content. 
• Recency: AI platforms look for more recent information from trusted sources to supplement their base knowledge, so in both owned and earned media, aim to publish fresh content recently.

In many respects GEO shares principles with good SEO practice. If you write consistently high quality content which addresses the needs of your target audience, and structure it correctly on well designed, standards compliant web pages, you will succeed.

How Can You Get Started With GEO?

Benchmark current GEO performance 

First you need to understand your starting point. Meltwater’s GenAI Lens is a tool that gives brands insight into how they are discussed by all major Generative Engines, including ChatGPT, Claude, Gemini, Perplexity, Grok, and Deepseek. 

With GenAI Lens you’ll be able to understand how your brand is already featured in LLMs, what source websites are used in those responses, and whether outdated or inaccurate information is being used. 

Audit online footprint

We know that LLMs pull content from the web, which they use to inform their responses. So whatever is said about your brand online will significantly affect how Generative Engines talk about your company. 

Start with your own website, this is the first place LLMs will look for information about your organization. But also focus on trusted, high authority third party sites like the mainstream news and specialist media within your industry, because these are the kinds of sources that LLMs will use. GenAI Lens can show you which websites the leading LLMS use to source information about your brand. 

Once you have a comprehensive audit of how your brand is currently portrayed online, you can perform a gap analysis against your desired messaging, and build a plan for how to reach that objective. 

Map audience queries

An important difference between GEO and SEO is understanding how people search differently when using Generative Engines. Users interact conversationally with AI, and are likely to ask more complex questions in natural language, compared to simple keywords or short queries in a search engine. So it’s useful to get an idea of what kind of questions people will ask about your brand or industry, to help plan your GEO content. 

Unlike search engines, at present the leading Generative Engines do not share data on popular user queries, so it’s not easy to find out exactly what people are asking about your business. But you can look at popular keyword searches associated with your brand, and brainstorm the most likely questions people will ask Generative Engines. 

You can also use a social listening platform, like Meltwater Explore+ to analyze social media conversations around your brand and industry, to gain insight into common questions. Once you have mapped out all of the most likely questions people might ask, you can start creating content that answers those questions. 

Align content to intent, not keywords

Conventional SEO was built around search queries but GEO requires thinking in concepts, context, and authority — you need to focus on the intent of the query, rather than simply trying to match keywords. 

LLMs prioritize sources that provide comprehensive, well-structured, and contextually rich content. Focus your content strategy on:

• Educational depth: Long-form resources that answer questions end-to-end. Publish explainers, FAQs, and data-backed insights that answer “how” and “why.”
• Credibility signals: Citations, quotes, and data from reputable sources (analyst studies, thought leadership, or trusted media).
• Rich media: Generative Engines pull information from videos and images as well as text content, so make use of varied content formats. 
• Consistency across channels: LLMs learn from media, social, and owned content together; ensure your narratives are aligned across them.

The key thing is to ensure that your content is genuinely helpful in answering those questions you mapped out. If your content is too focused on sales messages, it’s less likely to be used by the LLMs.

TIP: Discover innovative AI-generated content examples to enhance your digital strategy.

PR is essential to GEO

You can optimize the content published on your website and owned channels, but LLMs also source information from trusted third party websites. That means PR has a key role to play in ensuring your brand is featured in high authority earned media, including news features, expert comment from your spokespeople, product reviews, and any other kind of brand story traditionally generated through a media relations campaign. 

The tricky part is that unlike advertising, which can tell exciting and attention grabbing stories, PR professionals are limited to working only with the facts. But while you cannot always control the outcome of media outreach activity, ensuring that PR and GEO are aligned will help increase the volume of third party sources that feature the right messaging. 

Measure, iterate, improve

 Meltwater’s GenAI Lens gives you the ability to track your GEO performance over time, so you can understand whether your strategy is working well and spot areas where you need to improve. The tool provides visualizations that show brand sentiment, emotion, key phrases, people, products and things mentioned, alongside citations.

Armed with this information you can identify gaps in your GEO strategy where you might need to create more or better content. You can also discover which third party sources have the greatest impact on your GEO performance, and use that information to optimize PR outreach to focus on the most important media. 

Like SEO, this is a process of constant iteration and improvement. GEO should not be seen as a one-off project that will generate positive results in perpetuity, but rather an ongoing activity that needs to be integrated with the broader marketing mix.

Which Media Sources to Prioritize for GEO

When an LLM processes a user query, it can often detect that the response might require more recent information than it has access to in its original training data. In that situation it will look online for more up to date content that will help it to form a more accurate answer, but where exactly do Generative Engines look for this information?

At Meltwater we used GenAI Lens to research which content sources the leading AI platforms were most likely to reference in their responses, and there were distinct differences between them all:

• Open AI ChatGPT: Heavily favors text-based news and newswires (Reuters, AP, PR Newswire) and reference sites (Wikipedia). Much less likely to cite social media and UGC. 
• Google Gemini: Leans towards high quality UGC from sites like Reddit and YouTube, as well as other online sources within the Google ecosystem. Does not index X/Twitter content. 
• Google Search AI Overviews: Highly focused on UGC from Reddit and YouTube, as well as Facebook, Yahoo and LinkedIn. 
• xAI Grok: Strongly skewed towards conversational UGC from X and Reddit. Less likely to cite traditional reference sources like Wikipedia, YouTube or academic sites. 

Unlike SEO, where most practitioners were happy to optimize only for Google, in GEO we have to consider the nuances of several different platforms; at the very least ChatGPT and Google AI Overviews. In short, it’s good practice to ensure you have up to date content propagated across a wide range of platforms where LLMs might look for supplemental information.  

In terms of which online sources are most likely to be referenced overall, we looked at several categories and charted the most frequently cited websites:

6 Common GEO Mistakes to Avoid

1. Treating GEO like traditional SEO

Many teams focus on keywords, backlinks, and meta tags, but LLMs prioritize context, authority, and relevance. GEO requires content that answers full questions with clear, verifiable information, not keyword stuffing or link chasing.

2. Ignoring source credibility and transparency

AI models reward trustworthy, well-cited content. Publishing unverified claims, outdated statistics, or missing citations can lower visibility or lead to inaccurate AI summaries of your brand. Always include reputable data sources, authorship, and dates.

3. Failing to monitor how your brand appears in LLMs

Without ongoing monitoring, brands miss when GenAI models misrepresent their products or replicate misinformation. Tools like Meltwater’s GenAI Lens can surface these blind spots and help correct or update narratives quickly.

4. Producing siloed content across channels

If your website, press releases, and social media say different things, LLMs may produce inconsistent brand stories. GEO demands narrative alignment across earned, owned, and shared channels to reinforce message accuracy.

5. Neglecting freshness and structured data

Generative Engines favor current, clearly organized information. Outdated pages or unclear formatting make it harder for AI to interpret your content correctly. Keep publishing new, well-structured insights tied to recent data or trends.

6. Measuring the wrong success indicators

Traditional SEO KPIs like click-through rates or page rank don’t tell the full story. GEO should be measured by AI visibility share, narrative accuracy, sentiment, and engagement outcomes rather than traffic alone.

Avoiding these pitfalls ensures your GEO strategy builds trust, visibility, and consistency in how AI engines,  and their billions of users, perceive your brand.

What's the Difference Between GEO and SEO?

Search Engine Optimization (SEO) is a marketing discipline that has existed almost for as long as search engines themselves. The goal of SEO is to ensure that when a consumer searches for a topic relevant to your business, an appropriate page from your brand’s website is listed as close to the top of the search results as possible. SEO involves creating the right kind of content for your website, and ensuring that it is correctly structured, as well as encouraging high quality third-party websites to link to your own.

How is GEO similar to SEO?

GEO has similarities with SEO, they both rely on the production of high quality, well structured content that offers value to the reader. Both disciplines also require that relevant content be published on high authority third party sources, such as the media, and trusted industry websites. 

What are the key differences between GEO and SEO?

SEO is more focused on optimizing for specific keywords or search terms, while in GEO the emphasis is on creating content that is contextually relevant to the query, providing accurate and detailed information that will help give the user a useful answer. 

How does GEO impact SEO?

GEO does not replace SEO, and the two disciplines are complementary in many ways. For example, content that is built primarily for GEO purpose can still perform very well in conventional search. Equally, media outreach that secures positive brand coverage in trusted media channels can benefit both GEO and SEO.

Specifically, Google AI Overviews include summaries of content from top ranking search results, so in that situation strong SEO performance contributes to positive GEO.

8 Essentials of Generative Engine Optimization

1. Benchmark and measure progress

The first step of any GEO strategy is to understand where you’re starting from, so use a tool like Meltwater GenAI Lens to audit how your brand currently appears in leading AI platforms, and benchmark against your competitors. You can then use the same tool to measure the progress of your strategy, tracking improvements and identifying continued weaknesses. 

2. Answers, not keywords

Ask yourself this: What questions could our business be the answer to? Think of all the potential conversations a person might have with an LLM that your brand would like to be mentioned in. There’s probably a lot. Map them all out, and build a content plan that provides genuinely useful answers.

3. Messaging consistency

If there are conflicting facts about your brand scattered across different channels, and even inconsistencies on your own website, it will be more difficult for LLM’s to identify the truth. Build consistency into your comms strategy — wherever you talk about your brand, be sure to always get the facts straight. 

4. Authority and evidence

LLMs give more weight to information that can be verified through high quality sources and credible authors. Wherever possible link to high quality third party sources that back up any claims you make in your content. Also ensure the spokespeople you attribute content to have detailed author bios on your website, and build up their profiles on third party sources including LinkedIn and industry media.

5. Owned media 

Your website is the first place an LLM is likely to discover and absorb knowledge about your business, so make sure all of the information is constantly kept up to date and presented in clear, factual language that an AI can understand. Make sure it’s correctly structured too.

6. Earned Media 

Generative Engines rely heavily on authoritative independent media sources to add credibility and recency to their responses. It is therefore critical to ensure that your media relations activity is consistently placing accurate, on-message information in the press, because those stories will find their way into LLM responses. 

7. Community Engagement

Research shows that major LLMs draw a lot of their knowledge from discussions in online communities, such as Reddit and Medium. If you don’t already know which online communities are most relevant to your business, you should start researching them now, and build a plan for how you’re going to engage authentically with them. 

8. Broad content footprint

Most Generative Engines favor different sources when they look for more recent information to supplement their base knowledge. Some lean more towards major news sources, while others prefer social media, and it’s possible that they will update their preferences over time. So it’s important to ensure your content and messaging is seeded across a wide range of sources. You can also use GenAI Lens to identify which sources the Generative Engines are currently using.

Increase your GEO readiness with Meltwater

As generative AI transforms how audiences discover brands, Meltwater helps organizations prepare for the next evolution of search, GEO. Our AI-powered platform gives you the visibility, insights, and agility needed to understand how your brand appears across LLMS like ChatGPT, Gemini, Claude, and Perplexity. With Meltwater’s GenAI Lens, the industry’s first AI visibility tracking solution, you can see exactly how your brand, products, and competitors are represented in AI-generated answers,  identifying misinformation, emerging narratives, and new opportunities for visibility before they shape public perception.

Beyond monitoring, Meltwater empowers you to act. By unifying earned, owned, and social data through Explore+ and Mira, our intelligent AI teammate, teams can surface insights, analyze sentiment, and optimize messaging in real time. From enhancing content credibility to aligning global and local narratives, Meltwater provides a full-spectrum view of your digital footprint, ensuring your stories are consistent, trusted, and discoverable across both traditional search and generative AI ecosystems.

“Meltwater has the strongest product roadmap compared to other solutions, particularly in its understanding of AI evolution. They have very out-of-the-box thinkers, which is definitely why we chose Meltwater to be our partners for the long term.”

— Narek Garit, Global Measurement & Analysis Lead, HEINEKEN

Want to learn more about how GenAI Lens could kickstart your brand's AI visibility tracking? Click here and fill out the form to get a demo!

Generative Engine Optimization FAQ

What are the key challenges businesses face when integrating Generative Engine Optimization with existing SEO strategies?

The main challenge is being able to change the marketing mindset to the new approach: GEO focuses on context and credibility rather than keywords and backlinks. 

People can struggle to move on from conventional SEO paradigms, because they have been embedded for so long.  AI-driven Generation engines prioritize semantic relevance, authoritative sources, and up-to-date, multi-channel narratives. Limited visibility into how LLMs generate answers also adds complexity to optimization — SEO was well understood, but GEO is still an evolving practice. 

How do Generative Engine Optimization tools with real-time analytics help businesses measure campaign performance more effectively?

Tools like Meltwater’s GenAI Lens provide real-time visibility into how brands and competitors appear across major LLMs, tracking sentiment, emotion, and emerging narratives. This allows teams to quickly identify misinformation, optimize content strategies, and connect LLM visibility with performance metrics, proactively managing their brand reputation in the AI era. 

What success metrics should businesses track to evaluate the impact of Generative AI–driven search engine optimization?

Key GEO success metrics include:

• AI visibility share: How often a brand appears in LLM-generated answers.
• Sentiment and narrative accuracy: The tone and context of brand mentions.
• Engagement outcomes: Downstream traffic, conversions, or inquiries linked to AI-assisted discovery.
• Reputational indicators: Frequency of misinformation or outdated content.
• Speed to insight: How quickly teams detect and act on narrative shifts.

Together, these metrics show how effectively a brand is shaping its identity in the era of generative search.

How can businesses leverage GEO to improve local search visibility and audience targeting?

Businesses can use GEO to ensure their brand information, content, and context are clearly represented in AI-generated responses tied to specific locations. Be sure to include plenty of clear, GEO friendly content on your website that explicitly states the locations your business operates in. 

By optimizing owned content with structured data, local context, and consistent citations, brands can help LLMs surface accurate, localized insights, enhancing discoverability for region-specific queries and driving relevance in conversational searches.

How does GEO help brands appear in AI platforms like ChatGPT?

GEO ensures that AI platforms have clear, accurate, and up-to-date information to pull from when generating answers. Because LLMs rely on both training data and real-time retrieval from trusted websites, optimizing your owned content, earned media, structured data, and community presence increases the likelihood that ChatGPT and other AI engines will surface your brand in relevant responses. GEO strengthens the narrative signals that LLMs detect, helping them understand what your brand does, where it fits, and why it’s credible—ultimately making your business more discoverable inside AI-generated conversations.

What signals do LLMs use when selecting sources?

Generative Engines prioritize sources that are authoritative, recent, and structured in ways that are easy for AI to extract meaning from. Common signals include domain authority, citation frequency, recency of publication, clarity of formatting (e.g., headings, tables, bullets), and credibility markers such as authorship, links to reputable data, and transparent sourcing. LLMs also favor sources that are consistently referenced across the web and reinforced by multiple channels, including news sites, reputable blogs, community platforms like Reddit, and structured reference sources like Wikipedia.

What is the difference between Live Web Retrieval and Training Data?

Training data forms the long-term memory of an LLM—this is the massive dataset the model learned from during development. It includes foundational knowledge but does not update continuously. Live Web Retrieval, on the other hand, is the model’s short-term memory: it allows AI engines to fetch recent information from trusted online sources when a user’s question requires newer or more specific details than what exists in the training data. GEO influences both layers by strengthening your brand’s presence in the model’s base knowledge and ensuring fresh, authoritative content is available for retrieval.

What does GEO mean for PR teams?

GEO elevates PR from a brand visibility function to a core input shaping how AI engines describe a business. Because LLMs pull heavily from earned media—trusted news outlets, expert commentary, product reviews, interviews—PR becomes essential to influencing what narratives AI systems pick up. PR teams must focus on consistent messaging, authoritative spokespeople, and high-quality coverage to ensure LLMs cite accurate, up-to-date information. GEO also gives PR teams a new feedback loop: by monitoring AI visibility, they can identify misinformation, narrative gaps, and emerging risks before they impact reputation.

Does GEO improve Google rankings?

GEO does not directly improve traditional Google rankings, but the two disciplines support one another. Strong SEO performance can help your content appear in Google’s AI Overviews, which often pull from high-ranking search results. At the same time, GEO encourages clearer structure, fresher content, and higher authority—factors that can benefit organic search performance. While GEO is not a replacement for SEO, brands that excel at both are more likely to be surfaced in Google’s blended ecosystem of search results, AI summaries, and contextual answers.

What are the most common GEO mistakes?

Common mistakes include treating GEO like traditional SEO, relying too heavily on keywords instead of answering real user questions, and failing to monitor how LLMs currently describe the brand. Other pitfalls include outdated or inconsistent messaging across channels, low-quality earned media coverage, and ignoring which sources AI platforms actually cite. Many teams also overlook recency—LLMs strongly favor fresh content—and fail to track narrative accuracy over time, leading to blind spots in how their brand appears inside AI-generated answers.

How often should brands check their LLM visibility?

Brands should monitor their GEO visibility and AI narratives on an ongoing basis, ideally weekly or monthly, depending on how dynamic their industry is. AI models frequently update their retrieval patterns, and new media coverage, social conversations, or competitor narratives can quickly shift how LLMs describe a brand. Regular monitoring through solutions like Meltwater’s GenAI Lens helps teams detect misinformation early, understand when narratives change, and capitalize on new opportunities for visibility, ensuring their brand remains accurately represented as AI ecosystems evolve.