GEO vs SEO - What’s the difference and what should you do about it?
- Will Tombs
- 4 days ago
- 8 min read
Contents
Introduction
There is currently a huge amount of debate around GEO (Generative engine optimisation) and how similar it is to SEO (Search engine optimisation). Currently one of the most commonly used phrases in the organic search industry is: "Good SEO = Good GEO"
In this article, we'll analyse a real website with strong SEO but poor GEO visibility. We'll identify why this is and build a practical GEO strategy to fix this, explaining the difference between GEO & SEO along the way.
Video: GEO vs SEO - What's the difference & what should you do about it?
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What is SEO?
SEO (Search Engine Optimisation) is focused on improving visibility in traditional search engines like Google. It is built on three core pillars:
Content – creating relevant, helpful, keyword-targeted pages
Technical – crawlability, site speed, indexation, structure
Authority – backlinks, brand signals, PR and trust
Traditional search engines work through a process of:
Crawling – discovering pages
Indexing – storing and understanding them
Ranking – ordering results based on relevance and authority
SEO is fundamentally about ranking URLs for keyword-based queries.
What is GEO (Generative Engine Optimisation)?
GEO (Generative Engine Optimisation) focuses on visibility inside AI-driven search platforms like ChatGPT, Gemini (including AI Mode & AI Overviews), Perplexity, Grok and Claude.
Just like SEO, GEO also relies on:
Content – to influence training data and brand understanding
Technical – structured data, clear entity definition, semantic markup
Authority – citations, trusted sources, off-site validation
However, GEO operates very differently at the retrieval layer. AI engines do not “rank pages” in the same way Google does. Instead, they operate using:
Training data
Semantic association
Model recall
Prompt-driven retrieval, not keyword matching
You are not trying to rank URLs — you are trying to ensure your brand and products are recalled and cited correctly inside LLM responses.
Case Study: Home beer brewing product
The website we analysed is pinter.co.uk. Pinter is a home brew and draft system that allows people to brew different types of beer at home quickly and easily. Importantly, the technology is unique making it different from other products on the market.
From an SEO perspective, performance looks strong in Google UK:
#1 for “beer brewing machine”
Top 5 for “brew your own beer kit”
Top rankings across many related home brewing terms
Testing Pinter Inside AI Search (Prompt Analysis)
We then tested Pinter across a live series of prompts inside ChatGPT to understand how it performs in AI search. The exact prompts included:
“How to brew beer at home”
“Are there kits or products that make this easier?”
“What are the best easy brewing kits/products – ready in a few weeks?”
“What is the quickest and lowest effort type of brewing you can do at home?”
“What are the best just add water kits?”
“I don’t mind paying more (up to £200) for some equipment. Small batch brewing is ok”
“Just add water kits that allow me to brew and pour draft beer at home”
“Give me a longer list of brew and draft machines that allows me to brew and pour small batch at home”
“Give me a list of the top 50 brew and pour machines/kits/kegs that allows me to brew and pour small batch at home, up to 20 pints. Cost no more than £200.”
Across all 9 prompts, Pinter was not mentioned once.
Instead, GPT returned:
A list of competitor products
A strong category bias towards traditional brewing kits and kegs
It's important to note that we tested this series of prompts using slightly different variants and Pinter did appear in the responses. However, for the first instinctive set of prompts that was used, Pinter did not. So, this analysis does not confirm that Pinter has a major visibility issue in LLMs, but does indicate that further investigation needs to be undertaken to identify the extent of the issue and put a solution in place.
Identifying the Issue
When we pushed GPT on why Pinter was not being surfaced, the explanation was clear:
Pinter is not consistently recognised in the training data as a “home brew system”
It is often classified as a “beer appliance” instead
This response creates 2 questions:
1. What other category phrases is Pinter not associated with?
2. How do we influence the training data to fix this?
Fixing the issue: Step 1 - Prompt Research and Tracking
The first thing we need to do is quantify the visibility gap against certain phrases. We can do this through prompt research, enabling us to generate prompt suggestions and prompt volume estimations. Once we have the list we want to track, we can set this up to monitor visibility in Chat GPT and other LLMs on an ongoing basis. From this we can identify where visibility should exist but doesn't.
For Pinter, prompt analysis will identify phrases we need to be tracking, this might include:
Home brew starter kit
All-in-one home brewing
DIY beer brewing
Prompt research, suggestions, volume estimations and tracking are features available in most good GEO tools. At Buried we use Peec & Athena.
In summary, this stage will tell us exactly where the visibility problem exists.
Fixing the issue: Step 2 - Implementing a GEO strategy
Step 1 will tell us exactly where the visibility gaps are. Step 2 is all about building out content which helps the LLM to associate the site with these problem phrases. The 5 tactics below can all be used to help build the LLMs understanding of Pinter against these phrases and increase the chances of ranking.
1. Comparison Content vs Category Phrases
Comparative articles best published through the blog section of the website.
“Pinter vs home brew systems”
“Pinter vs brewing kits”
Etc - Pinter vs whatever the category phrase gaps turn out to be, identified in step 1
Purpose: To explicitly teach the LLM where the brand belongs by creating direct semantic comparisons to other category phrases where we are lacking visibility.
2. Educational Brewing Content
Educational articles best published through the blog section of the website.
How brewing systems actually work
Brewing vs fermenting: what’s the difference
Etc - focus on building expertise around the problem phrases identified in step 1
Purpose: To build deep topical authority and reinforce category relationships inside the model.
3. Category Language Across the Website
Work in category phrases to the other high priority areas of the website
Category landing pages
FAQs
Structured data
Purpose: To align the website’s product offering with the missing category phrases.
4. Inclusion in “Best Of” Articles on Third Party Websites
Target articles that compare the 'best' products in our target categories
“Best home brewing kits 2025”
“Best beer making systems”
Purpose: To create off-site training data signals that LLMs heavily trust.
5. Social & UGC Category Conversations
Generate social conversations that position Pinter in - or next to - a certain category
Reddit
Forums
Product comparisons such as:
“Is Pinter worth it vs X?”
Purpose: LLMs learn heavily from peer discussion and community-led content.
The interesting thing about all 5 of these GEO tactics is that they are all traditional SEO tactics.
However, without GEO analysis and tracking, we would never have been able to identify the opportunity or set the strategy
What have we learned: The similarities and differences between SEO & GEO
There is a huge amount of cross-over between GEO tactics and traditional SEO tactics
GEO optimisation is not possible through traditional SEO alone as the tracking, insight and analysis must be done through GEO tracking (prompts)
We would not have been able to create a GEO strategy (in the case study) using SEO data alone The number one SIMILARITY between SEO & GEO is that the majority of optimisation tactics are the same. Traditional and generative engines value great content, technical excellence and authority. The number one DIFFERENCE between SEO & GEO is that they require different tracking setups. The execution methods overlap heavily — but the measurement, prioritisation and retrieval layer are different.
Other differences between SEO & GEO
In GEO, you are optimising for citations and mentions but in SEO for keyword rankings. In SEO, success = rankings and traffic. In GEO, success = brand mentions, citations and inclusion inside AI answers. These mentions do not always link to a website, but they directly shape buyer discovery and brand recall.
Keywords and prompts have different structures
Keywords = shorter, structured, explicit search behaviour (SEO)
Prompts = longer, conversational, natural language, multi-intent discovery requests (GEO)
Prompts often contain: Multiple intents, Budget constraints, Effort levels, Time expectations, Category confusion
Keywords and prompts need to be tracked separately
Keyword rankings tell you how you perform in Google. Prompt visibility tells you whether LLMs understand, recognise and recommend you at all. At Buried, we use Athena and Peec to track: Brand mentions, Prompt-level visibility, Category-level performance. SEO and GEO tracking are not interchangeable — and treating them as such creates blind spots.
GEO is more difficult to measure
SEO: More reliable and establish metrics: keyword rankings, impressions, rankings, CTR, traffic, conversions.
GEO: Messier due to number of prompt variants and emerging metrics i.e. share of search, brand sentiment.
7. Search intent vs. decision influence
SEO: Most users still need to click to take action. You influence decisions after they land on your site.
GEO: Influence happens inside the answer itself. The model might recommend you before the user even sees your website. Your job becomes: shape the AI’s narrative about you.
Content evaluation logic is different
SEO:
Optimised around crawlers and ranking factors (keywords, backlinks, Core Web Vitals, schema, etc.).
Rewards technical precision and search intent matching.
GEO:
Optimised around model understanding.
Rewards clarity, structure, semantic richness, and authoritative positioning.
LLMs synthesize information rather than “rank” it, so your goal is to be easy to understand, remember, and reference.
GEO content should be:
More explanatory than keyword-heavy.
Well-structured with clear entity signals (definitions, comparisons, positioning).
Written to help AI models summarise you accurately.
Technical hygiene impacts LLM visibility, not just crawlability
Technical hygiene matters in SEO for crawling and ranking. In GEO, it determines whether LLMs can retrieve and use your data at all. For many queries, LLMs rely on structured data feeds and machine-readable sources. If those feeds are messy, inconsistent, or hard to parse, you’re not just ranked lower — you become invisible.
Taking the first step into GEO success
Our recommendation to every brand is simple:
Just start tracking!
If you start today, you’re already ahead of much of the market. Most businesses still have no visibility of how often they appear in LLMs, which prompts surface their brand, or when competitors are being recommended instead.
You don’t need a perfect GEO strategy on day one — you need a baseline. Tracking gives you:
Clear visibility of brand mentions in AI search
Insight into prompt-level performance
Early detection of category-level gaps
A great place to start is by reading our extensive GEO tool comparison article, which details features and functionality to tell you which tool is best for your business.
You can also reach out and speak directly with the Buried team to discuss tracking setup and GEO/SEO optimisation.