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Generative Engine Optimisation refers to the set of strategies used to optimize your brand's presence in AI-generated answers.
In the rapidly evolving landscape of online search, a new discipline has emerged: Generative Engine Optimization (GEO). GEO refers to the set of strategies used to optimize your brand's presence in AI-generated answers. As traditional SEO struggles to maintain traffic amid the rise of AI search engines like ChatGPT, Perplexity, and Google's AI Overviews, GEO is becoming essential for brands who want to stay visible in the age of large language models (LLMs). In this guide, we'll cover:
GEO stands for Generative Engine Optimization. It refers to the set of strategies used to optimize your brand's presence in AI-generated answers, especially those delivered by large language models like ChatGPT, Claude, or Gemini. Unlike SEO, which is about ranking web pages in search engine result pages (SERPs), GEO focuses on ranking entities (brands, products, people) within AI-generated content. These models don't just provide links - they summarize answers, often pulling information from multiple sources and delivering it conversationally. That means if you're not mentioned in the response, you're invisible.
AI-driven search engines are exploding:
According to Semrush (April–May 2025) data, ChatGPT's website chatgpt.com ranked #5 worldwide, with approximately 5.24 billion monthly visits, sitting right behind Instagram and ahead of Wikipedia.
A Search Engine Journal report (Jan 2025) states that AI Overviews appear in about 30 % of searches, and are triggered in **74 % of problem-solving queries. Source : **SearchEngine Land
With less visibility and fewer clicks from traditional Google search, brands must shift their focus. GEO is no longer optional - it's your new visibility strategy.
Traditional SEO focuses on optimizing pages. GEO goes further - it's about influencing how AI understands and references your brand in its own words. For detailed methods to discover the prompts users ask about your industry, check our comprehensive guide: How to Identify Prompts to Track on AI Search Engines . To help you we built an entire dashboard about the differences between GEO and SEO :
Dimension | SEO (Search Engine Optimization) | GEO (Generative Engine Optimization) |
Objective | Rank web pages in Google/Bing results | Be cited or ranked in AI-generated responses (text, cards, summaries) |
User Interface | List of links (SERP) | Direct answers with citations, branded mentions, or summaries |
Optimization Target | Keywords, technical structure, links | Entities, facts, clarity, prominence of brand mentions |
Link Importance | High - backlinks are central for PageRank and authority | Medium - links are used mostly as source signals, less for direct traffic |
Brand Mentions | Helpful, but secondary to backlinks | Crucial - LLMs rely heavily on co-occurrence and entity associations |
Content Granularity | Pages optimized for a keyword | Passages optimized for a question or prompt |
Click Behavior | Users choose what to click | Often zero-click - the answer is given directly |
Update Cycle | Fast: new content can rank in days | Slower for static LLMs (e.g. Claude), but fast for AI searches (e.g. ChatGPT Search, Perplexity) |
Citations | Not required; Google rarely cites sources | Essential in hybrid LLMs (ChatGPT, Perplexity), with inline citations or footnotes |
UX Factors (CTR, Bounce Rate) | Significant - affects rankings | Unknown or minimal (at least for now) |
Technical SEO | Crucial: structure, schema, speed, robots.txt | Important for crawlability, especially to LLM bots (e.g. GPTBot, ClaudeBot) |
Impact of Schema Markup | Enhances SERP appearance (FAQ, stars) | May influence how content is interpreted by LLMs (FAQ schema, definitions) |
Discovery Process | Index \> Rank \> Click | Crawl \> Ingest \> Generate answer from internal + external sources |
Feedback Loop | Through CTR, engagement, and Google updates | Through RAG systems, citations and retraining (for static LLMs) |
Strategic Horizon | Medium to long term | Long term (training impact) + short term wins via source ranking |
There are two types of LLMs:
In this part of the article we will give you a detailed step by step process to create your first GEO strategy. If you want to learn more about how to create a GEO audit you can read our guide: How to rank your brand on LLMs .
Identify the actual prompts and questions your audience asks LLMs (not just keywords), to ensure your brand appears in their answers.
Run a free audit: in a few minutes, ChatGPT, Gemini and Perplexity answer 8 buying questions about your market live, and you see who they recommend.
Unlike traditional SEO, where users search with 2–4 word queries, LLM interactions are conversational and complex. That changes everything. LLMs are designed to respond to prompts like:
These aren't just keyword searches - they're natural language questions, often reflecting a deeper intent (comparisons, opinions, recommendations).
Qwairy analyzes your brand visibility across LLMs and shows you:
Even if GSC doesn't show LLM data directly, it reveals natural language queries users type on Google, many of which also surface on ChatGPT.
Use these REGEX filters:
^(who|what|where|when|how|why|can|should|best|top|compare)+\s
And for comparison-style prompts:
^(best|vs|or|top|cheapest|most recommended)+\s
Export queries from GSC, clean them, and match them with ChatGPT/Perplexity behavior.
Start typing your industry topic and let ChatGPT suggest completions. This gives a real-time sense of how users formulate prompts.
Example: Type "best CRM for" and you might see:
- "... for small business"
- "... for agencies"
- "... with email automation"
These are pure gold for content targeting.
When you search in Perplexity, it often shows:
Use it to reverse-engineer clusters of prompts around your brand or offer.
By the end of Step 1, you should have:
Before you can improve your presence, you need to know where you stand. This step is about measuring how often and how well your brand is mentioned in LLM-generated content - either as a source or as a brand.
In LLMs, your brand can appear in two primary ways:
These two appearances are not interchangeable - they impact brand visibility differently. A source citation may bring referral traffic and authority; a brand mention reinforces your recognition and trustworthiness.
The most efficient way to monitor your presence is to use a specialized tool like Qwairy. It scans across major LLMs (ChatGPT, Gemini, Claude, Perplexity, etc.) and tells you:
Qwairy provides a "visibility matrix" that can be filtered by LLM, prompt category, or type of mention. This gives you a clear map of where you stand and where to act.
If you don't have access to such a tool, it's still possible to do it manually - though time-consuming. You can prompt LLMs with queries you've gathered (from Step 1), and then manually record where your brand appears and in what form. Tools like ChatGPT Pro, Perplexity, and even Bing Chat (Copilot) allow you to simulate these queries across platforms.
You're not just checking if your brand shows up - you're looking at:
Mapping your competitive landscape is key. Sometimes, the most visible brands in LLMs aren't the ones dominating traditional SEO. US-based or Wikipedia-cited brands often show up more frequently due to their presence in training data.
After this audit, build a dashboard or spreadsheet that tracks for each prompt:
This becomes your tactical roadmap for content and authority optimization.
Don't assume that if you rank #1 on Google, you're visible in ChatGPT or Gemini. LLMs pull from a mix of structured training data, trusted domains, and co-occurrence - not from SERPs. Also, don't rely solely on brand name searches. LLMs won't mention you unless your brand is relevant and authoritative on the topic.
Now that you know which prompts matter and where your brand is (or isn't) mentioned, it's time to act. This step is about crafting or refining content so that LLMs identify it as relevant, trustworthy, and worthy of inclusion in their answers.
LLMs don't evaluate your content like a Google algorithm. They look for semantically rich, clearly structured, and contextually relevant passages, not just pages. What matters is not just "having content" on a topic, but providing clear, answer-oriented segments that LLMs can reuse. Let's take a prompt like "What is the best CRM for freelancers?" Ask yourself: Do I have a page or article that clearly and directly answers this question? Is the passage structured in a way that LLMs can easily extract? Does my content include comparative reasoning, expert quotes, or statistics? 💡 If you want to understand how LLMs talk, you can read our study where we analyzed 32, 961 queries made on ChatGPT, Perplexity and Gemini
Start from scratch with a GEO-first mindset. That means:
You need to re-optimize for extraction. That means:
LLMs may favor pages that answer multiple related prompts. Think cluster topics. A page on "Best CRM for freelancers" might also answer:
See the web searches captured behind answers from supported models, and the content gaps they reveal.
SEO tells you to create evergreen, high-intent landing pages. GEO tells you to write extractable insights. SEO cares about headers, keywords, backlinks. GEO cares about clarity, citations, structure. In SEO, users find your page. In GEO, LLMs decide if your answer deserves to be summarized.
Once your content is live, test it. Paste the prompt into ChatGPT or Perplexity. Does it now mention your brand or cite your page? If not, tweak your copy and submit feedback to the LLM when possible (ChatGPT and Perplexity allow this). Over time, these reinforcements help the model associate your content with the prompt.
To rank in LLMs, your brand must be perceived as trustworthy. Unlike SEO where backlinks are the ultimate currency, GEO gives more weight to brand mentions, source citations, and presence in training data. You can see all the sources used by an LLM on Qwairy:
LLMs don't just pick random content - they generate answers based on a mix of:
So the real question is: When a user asks a question in your industry, is your brand part of the model's known and trusted ecosystem?
Models favor information from domains like:
You don't need links - just named mentions. 💡 Ex: "According to the French startup Qwairy, GEO is the new SEO for AI." That sentence, even without a backlink, feeds entity recognition.
Wikipedia is core training material for most LLMs. If you can:
This dramatically increases your entity presence in LLM memory.
LLMs are trained on Reddit, Quora, Stack Overflow, and even niche forums. Strategically placing content here increases:
Use a pseudo if needed, but create threads that answer key prompts from Step 1.
Even though backlinks matter less directly, cited sources do influence how hybrid AI searches (like Perplexity) form answers. You should:
Tools like HARO, Featured.com, and Qwoted are helpful to place expert quotes in articles.
Make sure your site is crawlable by:
Check your robots.txt and server logs. Don't block these bots unless you have a legal reason.
User-agent: GPTBot
Allow: /
User-agent: ClaudeBot
Allow: /
This ensures your content is ingested in future model training.
📚 Bonus Tip: Use Perplexity's citation panel to spy on what sources get chosen - and aim to replace or supplement them with your own content.
Metrics to follow:
Set up filters in Google Analytics using Regex like:
^.*ai|.*chatgpt.*|.*gemini.*|.*perplexity.*|.*openai.*
Monitor and optimize weekly.
GEO is where SEO was in 2005 - a golden opportunity for early adopters. If your brand wants to remain visible in a world dominated by AI-generated answers, now is the time to act. Start by analyzing your current LLM visibility. Build authoritative, structured, conversational content. Earn brand mentions in credible places. And keep your finger on the pulse of evolving AI search platforms.
GEO isn't a trend. It's the next frontier of online visibility.
Track the questions that matter to your business, see which brands each answer mentions and which sources it cites, then work on the gaps.
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