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All You Need to Know About GEO
As a search engine optimisation (SEO) agency, we have spent decades navigating the volatile shifts caused by Google’s algorithm updates. Despite this, our team of SEO experts have been not only sustaining but growing client SEO rankings throughout the years. Nevertheless, the transition from traditional search to Generative Engine Optimisation (GEO) represents the most significant paradigm shift since the induction of SEO.
Forget scrolling, clicking and scrolling again. For decades, SEO was about securing a spot in the top positions of the search engine result pages (SERPs). Users search using keywords and click into search results, and if the listing doesn’t have the content that they want, they’ll have to go back to the SERP and look again.
However, thanks to the rise of Large Language Models (LLMs), there is now a fundamental shift in consumer search behaviour. From ranking on the first page of Google to being mentioned in AI results, this shift in the search marketing landscape is indeed significant.
Today, the difference in search behaviour is clear. Users aren’t only looking for website results; they are looking for immediate answers. Not only do marketers and business owners have to rank their content on the SERPs, but they also have to contend with hundreds, maybe thousands of other businesses to become the primary source that AI “thinks” with in order to establish SEO dominance.
This is why brands must pivot to stay relevant, especially if they want to be discovered by their customers. To do this, we’ve created a guide to GEO to help brands better understand how to navigate complexities in AI content optimisation.
Understanding GEO: Search Behaviour Redefined
GEO Definition
GEO is the process of optimizing digital content so that information is retrieved, synthesized, and cited by AI-powered search engines and Large Language Models (LLMs). It’s essentially a framework designed to make digital content more “digestible” and “authoritative”.
Unlike traditional SEO, which focuses on driving traffic to a specific URL through the SERPs, GEO focuses on being cited by AI.
LLMs like Gemini, GPT-4 and Google’s AI Overview use Retrieval-Augmented Generation (RAG). When a query is made, the AI searches the web for high-authority snippets, ’reads’ them, and generates a response. To be part of that response, your content must be easily ’scraped’ and understood by these models.
Understanding this is important to be able to optimise your website for GEO.
From Keywords to Concepts: An Evolution from SEO
From Retrieval to Synthesis
SEO was built on a retrieval model, while GEO operates on a synthesis model.
In the SEO retrieval model, a user inputs a keyword, and the search engine retrieves a list of relevant information displayed on the SERP. In the GEO synthesis model, instead of matching a keyword to a URL, the model identifies the ‘subject’ and ‘intent’ within your query and builds a response in real-time.
This is why with SEO, users who are seeking an SEO agency may search for ‘best SEO agency’, while in the context of GEO, users with the same intent tend to use different phrases such as ‘give me a list of the best SEO agencies with explanations on the selection criteria for each’.
From Clicks to Citations
In SEO, a common analogy used is the ‘Library’ model where search engines, such as Google, point the user to a book. With GEO however, the Tutor analogy would be a better fit where the LLM reads the book for the user and summarises the details.
This is why for SEO, ranking on the first page of Google, or on the top 3 or first position was the gold standard. In GEO, however, the primary goal is the AI Citation Rate—how often the model mentions your brand in its results. Dubbed the ‘Zero-Click’ Phenomenon, LLMs aim to answer the user’s question immediately within the interface. The advantage? Appearing as the cited source in an AI response puts your website or content in front of your potential customer, before they even get the opportunity to scroll or click.
While the terminology used may suggest that sources used for citation by LLMs don’t get any clicks, it’s important to note that the Zero-Click phenomenon applies predominantly to informational search intent (or research keywords). This doesn’t apply to potential customers who are further down the marketing funnel. For instance, a user may be comparing products and/or services (consideration), or could have already decided that they are going to make a purchase or enquiry (conversion). In such scenarios, users would still have to click into the website or visit the physical shop in-person.
AI Content Optimisation: How to Optimise for GEO?
AI Content Optimisation Basics
In order to understand AI content optimisation, it’s important that we talk about semantic and lexical search, because both play a big role in SEO.
Lexical Search: Literal Keyword Matching
In the early days, search engines such as Google relied on lexical search (also known as Keyword Matching). Using a process called Term Frequency, lexical search counts how many times your search terms appear in a document. This means that the more times those specific words appear on a page (and in the right order), the higher that page ranks.
| Period | Marketing Channel | Where do Results Appear? | Process |
| 1990s to early 2010s | SEO | Appears on the Search Engine Results Pages (SERPs). | Lexical Search(Early search engines). |
| Early 2010s | SEO | Semantic Search(With the introduction of Google’s Knowledge Graph) | |
| Mid 2020s | SEO | Semantic Search(Via Google’s Algorithms such as Google’s Knowledge Graph, Hummingbird, RankBrain, BERT and MUM etc.) | |
| GEO | Appears on AI citations (e.g. Google’s AI Overview, Perplexity AI or Chat GPT etc). | Retrieval-Augmented Generation (RAG)(With the introduction of LLMs) |
The result? When users search for ‘male soccer shoes’, the search engines return results that mention ‘male’, ‘soccer’ and ‘shoes’, instead of listing results that specifically focus on ‘male soccer shoes’. This means that users may get webpages that feature formal shoes on their SERP rather than soccer shoes, since both types of pages contain the word ‘shoes’. Users could also see results for ‘soccer’ and ‘male’ which may not necessarily contain information on ‘male soccer shoes’.
| Period | Search Keyword | Possible Search Results on the SERPs. |
| 1990s to early 2010s | ‘Male soccer shoes’ | An article featuring the world cup. |
| An eCommerce website that features formal shoes. | ||
| A webpage explaining the definition of ‘male’. | ||
| A retail store webpage featuring the product category ‘male soccer shoes’. |
See why that could be a problem?
To improve the relevance of search results, the semantic web concept was conceived, and in the early 2010s, Google incorporated semantic search to improve the relevance of its search results.
Understanding Semantic Search
If SEO was about retrieving results based on keywords, Semantic Content Creation is about building content for the entire ecosystem of that word. This isn’t new. Google has been incorporating Semantic search into its search engine since over a decade ago with the introduction of algorithms (e.g. Knowledge Graph, Hummingbird, RankBrain, BERT and MUM), way before the AI Overview was introduced.
But first, let’s understand the basics.
What is Semantic Search?
Semantic search is a data searching technique that uses natural language processing (NLP) and artificial intelligence to understand user intent, context, and the conceptual meaning of words, rather than just matching literal keywords.
In the GEO framework, semantic search acts as the retrieval layer. When an AI generates an answer, it uses semantic search to find the most relevant, trustworthy “entities” (people, places, or concepts) to build its response.
Today, search engines like Google (via AI Overviews) and LLM models like ChatGPT don’t just “match” keywords (as we’ve seen above). They perform entity extraction. If your content doesn’t follow a clear hierarchy, the AI cannot “lift” your information into its synthesized answers.
This is why understanding semantic entity classes is essential to write optimal content that maximises GEO and SEO results.
Semantic Entity Type Example: Ice Cream
| Keyword / Topic | Level | Semantic Entity Type | Semantic Entity Class |
| Ice Cream | 1 | Root Entity | Thing |
| 2 | Broad Category | Product | |
| 3 | Sub-category | Dessert | |
| 4 | Niche Category | Frozen Dessert | |
| 5 | Core Entity | Ice Cream |
Using the topic / keyword ‘ice cream’ as an example, here’s a taxonomy map that our team uses to improve AI citation rates for GEO based on schema.org guidelines:
| Keyword / Topic | Semantic Entity Type | Semantic Entity Class | Examples |
| Ice Cream | Root Entity | Thing | Soft Serve, Gelato, Scoop, Pint, Cone etc |
| Broad Category | Product | Consumable, Merchandise etc | |
| Sub-category | Food & Beverage | Frozen Dessert, Dairy, Vegan etc | |
| Niche Category | Form Factors | Soft Serve, Gelato, Scoop, Pint, Cone etc | |
| Semantic Relationships & Context | Occasions | Ice Cream Parlor, Dessert Shop etc | |
| Brands | Ben & Jerry’s, Häagen-Dazs, Lickers etc | ||
| Metaphorical/Abstract Semantics | Sensory/Metaphorical | “Smooth like vanilla ice cream” (effortless) etc | |
| Personality Theory | Personality Theory: “Ice cream theory” etc |
Understanding NLP (Natural Language Processing)
NLP (Natural Language Processing) is a branch of Artificial Intelligence (AI) that gives computers the ability to understand, interpret, and generate human language. Between how people communicate and how computers process information, NLP acts as the bridge. In the early days of Google, before the era of NLP, Google simply matched your keywords to search results. Today, NLP allows search engines and LLMs to move beyond text strings to identify entities.
Why is NLP relevant to Semantic Search and GEO?
Semantic search uses Artificial Intelligence and Natural Language Processing (NLP) to understand the intent of the searcher and the contextual meaning of the terms used. Because AI models (LLMs) are essentially giant NLP engines, they only “trust” and “retrieve” content that is structured for their understanding.
This is why black hat SEO techniques that solely focus on increasing the density of a particular keyword (such as keyword stuffing, or using hidden keywords), which may have worked in the early days of Google, aren’t just outdated—they are a liability. Semantic optimisation doesn’t just involve building a topical universe around the keyword that you want to rank, semantic optimisation is the future of SEO, especially with the introduction of LLMs and Google’s AI Overview.
For more information on the SEO mistakes to avoid, check out this article.
6 Content Optimisation Strategies for GEO
1. Implement Semantic Content
Regardless if you’re optimising for SEO or GEO, semantic content is indispensable to success. For example, to be cited for a topic such as ‘preschool Singapore’, LLMs expect to see semantic terms in your website like ‘inquiry-based learning’, ‘bilingual immersion’, ‘holistic development’, ‘STEM preschool’ etc.
2. Use Domain Specific Terminology
Use domain-specific terminology. Research shows that using domain specific terms correctly can increase AI citation probability. For instance, to be cited for a topic such as ‘preschool Singapore’, LLMs expect to see domain specific terms in your website like the ‘SPARK Certification’, ‘EYDF (Early Years Development Framework)’. If these aren’t present, AI or search engines may flag your content as ‘low trust’.
| Keyword / Topic | Domain Specific Terminology |
| Preschool Singapore | SPARK Certification |
| EYDF (Early Years Development Framework) | |
| SPARK 2.0 (Singapore Preschool Accreditation Framework) | |
| HoneyKids Asia Singapore Education Awards | |
| EDCA (Early Childhood Development Agency) |
3. Cite Credible Sources
In the world of SEO and GEO, information density is the new currency. Research has shown that citing credible sources via authoritative data, statistics and studies increases citation probability. What does this mean? Between marketing “fluff” and hard statistics, search engines and AI models now prioritise the latter.
At Rogue Digital, we help clients with website content creation based on authoritative data. This not only serves as a signal of trust in the eyes of search engines like Google, but also serves as an anchor for AI to verify your content against content on the web. The result? Stronger and faster SEO rankings, and higher AI citation rates.
4. Local Intent Synthesis
This means that if you’re a tuition center with multiple locations across Singapore, don’t just say ‘Singapore’, mention specific preschool locations wherever your centres are located (e.g. Punggol, Tengah etc). This helps AI with local intent synthesis, which impacts your AI citation rates.
5. Structure for Retrieval-Augmented Generation (RAG)
Most modern AI search engines use a process called RAG. When a user asks a question, the AI searches the live web, retrieves relevant snippets, and then writes an answer based on those snippets. To be “retrievable,” your content needs to be machine-readable.
Lead with the Answer
Use the “Inverted Pyramid” style. Answer the primary question in the first 50–60 words of your section. AI models are more likely to cite content where answers are placed in front.
This doesn’t only apply to GEO, but it also applies to SEO. With SERP features such as Google’s Answer Box, creating content that leads with an answer allows you to optimise your content for both SEO and GEO.
Use Question Headers
Phrase your headings as the exact questions your audience is asking (for example if you’re selling solid wood dining tables, “How much does a solid wood dining table cost?”).
6. Create Natural Language Processing (NLP) Ready Content
Natural Language Processing (NLP) is a branch of Artificial Intelligence that gives computers the ability to understand, interpret, and generate human language (text and spoken words) in the same way human beings can.
To be ‘NLP-ready’, content should follow a logical flow. Apart from leveraging direct answers, use bullet points and tables, which are easier for AI to extract and reformat into its own answers. Again, AI models are trained to prioritize neutral, factual language over marketing “fluff”.
Technical Optimisation
Structured Data and Schema Markup
Schema markup acts as a direct data feed for AI. Through the use of structured data, AI can better identify your products, articles, reviews, and authors, essentially reducing the guesswork needed, which increases the likelihood of the LLM synthesizing your content. This also increases the likelihood that the specific information pertaining to your products and/or services are cited accurately.
If you’re an office furniture brand that wants to optimise for the keyword / topic ‘ergonomic chairs’, here’s how the taxonomy map can look like:
| Keyword / Topic | Level | Semantic Entity Type | Semantic Entity Class | Semantic Sub-Entity Type | Examples |
| Ergonomic Chairs | 1 | Root Entity | Thing | N/A | Task, Executive etc |
| 2 | Broad Category | Product | Frame Material | PVC, Aluminum etc | |
| 3 | Sub-category | Materials | Glass-fiber Polyamide, Die-cast Aluminum etc | ||
| 4 | Niche Category | Core Mechanisms | Weight-sensitive Tension, Forward Tilt etc | ||
| 5 | Core Entity | Individual Product | Brand | Aeron, Harunamu, Secret Lab etc | |
| Upholstery | Mesh, Leather, Fabric etc | ||||
| Adjustments | 6D Armrests, Height-adjustable Lumbar Support, Seat Depth Adjustment |
If you notice, the sub-type for ‘ergonomic office chair’ at level 1 (entity type) is simply ‘ergonomic chair’ in the table above. Why? Ice cream has sub-types (like Form Factors) because the way it is served changes the user intent and the search result.
Leverage Entity-Based SEO
Search engines now organize information into a “Knowledge Graph” made of Entities (unique objects or concepts). Brands that consistently appear in authoritative databases (Wikipedia, LinkedIn, Industry Directories) strengthen their “Entity” status, making them a “trusted” source for not only search engines but AI models
AI Visibility Mistakes to Avoid
These mistakes don’t just apply to AI visibility, but they are also relevant to SEO.
Using Images Over Text
Do not place important text in images only, as search engines bots and LLMs likely won’t read text in images on your website. Even if they do, the data extracted may not be as accurate, resulting in a broken visual-semantic link.
Utilising Hidden Tabs
Avoid having crucial information hidden within answer tabs or expandable menus. Why? Search engine bots and LLMs may not crawl text that’s hidden within expandable sections reducing the effectiveness of your content.
Relying on the ‘Wall of Text’
Minimise the use of long paragraphs as it’s harder for your content to be crawled. This is because long paragraphs or a wall of text could increase the noise to signal ratio, resulting in difficulties in extracting the information from your content. A general rule of thumb to follow would be to avoid having 3 or more sentences in a paragraph. Naturally, consider your brand tone of voice (TOV) as well.
Placing Crucial Information in PDFs Exclusively
PDFs do not contain schema.org meta data. Signals such as headings (e.g. h2, h3) are also not found within PDF files. This is why important information shouldn’t only be placed in PDFs, they should also be placed in HTML web pages. Think about it, would it be easier for AI to pull data and synthesise an answer from a HTML page or a PDF file?
Incorrect Schema Implementation
Ensure that your schemas are implemented accurately. Otherwise, you may be sending signals of bad faith to search engines and LLMs. Some examples include:
Base Price vs. Variant Discrepancy
Ensure that your content matches your schema meta data. For instance, if your webpage contains the schema “starting from” price of $100 but the price stated on the same web page is $200, your content could be flagged as deceptive by LLMs which reduces the credibility of your content.
AggregateRating Mismatch (“Ghost” Reviews)
If your schema ‘AggregateRating’ doesn’t match the reviews displayed on your website, search engines and LLMs may treat your data as inaccurate, reducing the credibility (and visibility) of your content. For instance your schema ‘AggregateRating’ says 4.5 stars while the reviews on your webpage say 5 stars.
Availability Mismatch (ItemAvailability :: InStock)
For eCommerce websites, ensure that product availability matches with ‘ItemAvailability’. If your webpage indicates that your product isn’t available but the schema reads ‘InStock’, the credibility of your web page will be lowered which in turn, could negatively affect AI visibility.
Conclusion
As the world moves towards GEO, driven by the proliferation of LLMs, digital marketing, as we currently know it, is undergoing unprecedented change. The good news is that while the goal posts may have changed, many of the fundamentals behind SEO remain intact, carrying forward into GEO in ways that are correlated and symbiotic.
As AI synthesizes answers for its users, your brand’s goal is not only to be the most authoritative piece of content that AI recognises (and synthesises its answers from), but to also remain visible, both on AI overviews, and on the SERPs. In short, despite the rise of GEO, SEO isn’t dead, in fact SEO is still crucial to drive brand visibility, leads and sales.
At Rogue Digital, we focus on technical and semantic content optimisation strategies that ensure your brand is the one search engines and AI choose to amplify. Speak with our team of SEO and GEO specialists to understand how we can improve your brand’s visibility on search engines and LLMs today.