Agencies
AEO, GEO, LLMO: Do These Terms Mean Different Things?
Jillian Oco, CMO— AI-assisted and reviewed prior to publication.
Yes, they mean different things in theory but describe nearly identical work in practice. No consensus definition distinguishing these terms had been established in the academic literature as of early 2026, and the terms are frequently used interchangeably in trade and practitioner contexts.
Where each term came from
GEO was formally defined in November 2023 by Princeton University researchers. LLMO emerged from practitioner communities around the same time. AEO predates both but has been repurposed for AI contexts (onely.com). That explains why agency owners hear all three in the same conversation, sometimes within the same sales call.
The terminology is new, unstandardized, and applied inconsistently across the industry. Different communities approached the same problem from different angles (SEO practitioners, AI researchers, tool vendors), each naming the part they cared about most. The result is three labels that sound distinct but describe work that mostly overlaps.
What does each term technically mean?
Answer Engine Optimization (AEO) is the practice of structuring and formatting content so AI-powered tools like ChatGPT, Perplexity, Google AI Overviews, and voice assistants can easily understand, trust, and cite it as direct answers to user queries (tryprofound.com).
Generative Engine Optimization (GEO) is the process of strategically creating and refining your website content so that answer engines and AI chatbots can effectively understand, surface, and present it to users (conductor.com).
LLMO (Large Language Model Optimization) is the practice of optimizing content and websites to increase visibility and citations within large language model outputs. LLMO is essentially synonymous with Generative Engine Optimization (GEO), with the emphasis placed specifically on optimizing for the underlying language models that power these AI systems (conductor.com).
Where do they actually differ?
AEO differs more meaningfully. While GEO and LLMO optimize for conversational AI responses, AEO primarily targets traditional search interfaces: featured snippets, voice results, answer boxes. AEO work tends toward concise, structured Q&A formatting. GEO and LLMO tend toward depth, comprehensive coverage, and topical authority signals.
GEO and LLMO share approximately 80% functional overlap. Both optimize for AI citation. The distinction is primarily origin (GEO from academia, LLMO from practitioners) and scope. GEO encompasses all generative engines, LLMO specifically targets large language models.
For most agency owners selling to small businesses, the practical difference is negligible. The same page that ranks well, wins a featured snippet, gets cited in the free AI-visibility check (/check), and shows up when ChatGPT recommends a local plumber would count as success in all three frameworks.
Why the label matters less than the measurement
These terms describe the same underlying practice with slightly different historical baggage. The differences matter less than the consultant industry pretends (contently.com). What matters is whether your clients show up when someone asks an AI who to hire.
The fundamental difference between AEO and SEO is the goal. SEO's goal is to drive qualified traffic, measured by rankings and click-through rates. AEO's goal, in contrast, is to increase visibility in AI answers, measured by mentions and citations (hubspot.com). That shift from clicks to citations applies equally whether you call it AEO, GEO, or LLMO.
Small business clients do not care which acronym you use. They care whether ChatGPT, Perplexity, Gemini, and Claude name them when a potential customer asks for a recommendation. Measuring that visibility across those four platforms, the way ActiScore's pricing (/pricing) makes possible with white-labeled reports starting at the Starter plan, answers the question that actually drives revenue.
AI search optimization is the process of making your content easy for AI-powered search tools to find, understand, summarize, and cite. These names describe related ideas, but businesses should not get distracted by labels.
What to optimize for regardless of what you call it
These three frameworks are not separate strategies. They are concentric circles with a massive amount of overlap. Almost everything you do for AEO also helps with GEO. And almost everything you do for GEO also contributes to LLMO.
The core tactics remain consistent across all three labels: write clear answers to specific questions early in each section, use structured data and schema markup, build topical authority through depth rather than keyword repetition, earn citations from authoritative third-party sources, and keep content updated.
Optimization tactics overlap significantly: structured content, authority signals, and entity optimization benefit all three. Ahrefs found that 76% of AI Overview citations pull from pages already ranking in Google's Top 10. That means traditional SEO fundamentals (crawlability, clean technical structure, backlinks, E-E-A-T) remain the foundation, regardless of whether the end goal is a traditional click or an AI citation.
The work is the same. Start with solid SEO, then optimize for extractability. Use clear headings, answer questions directly in the first two or three sentences of each section, structure comparisons as tables, and cite your own sources. A page built that way performs across traditional search, featured snippets, and AI-generated answers at once.
How to talk about this with clients
Pick one term and use it consistently in your own proposals and reports. GEO is the most defensible choice because it has academic backing and covers the broadest range of AI systems, but LLMO and AEO work just as well if your team already uses them.
When a client or prospect uses a different term, translate without correcting them. The point is the work, not the vocabulary. If they ask whether your agency does "AEO" and you've been calling it "GEO," say yes and move on. The strategies are functionally identical.
What clients actually need to understand is simpler: when someone asks ChatGPT or Perplexity for a recommendation in their category, does their business get named? The guide (/guide) walks through how to check that, what signals increase the odds, and where to focus effort first. That conversation matters more than debating acronyms.
Common questions
What is the difference between AEO and GEO?
AEO differs more meaningfully from GEO in its target. While GEO optimizes for conversational AI responses, AEO primarily targets traditional search interfaces like featured snippets, voice results, and answer boxes. AEO work tends toward concise, structured Q&A formatting, while GEO tends toward depth, comprehensive coverage, and topical authority signals. However, for most practical purposes, the same page that performs well in traditional search will also succeed in AI citations regardless of which framework you follow.
Are GEO and LLMO the same thing?
GEO and LLMO share approximately 80% functional overlap and are essentially synonymous. Both optimize for AI citation and work with the same underlying systems. The distinction is primarily origin, with GEO emerging from academia at Princeton University and LLMO from practitioner communities. GEO encompasses all generative engines as a broader category, while LLMO specifically emphasizes the underlying large language models that power these systems. For most agency owners selling to small businesses, the practical difference is negligible.
Which term should I use with clients: AEO, GEO, or LLMO?
Pick one term and use it consistently in your own proposals and reports. GEO is the most defensible choice because it has academic backing from Princeton University researchers and covers the broadest range of AI systems, but LLMO and AEO work just as well if your team already uses them. When a client uses a different term, translate without correcting them since the strategies are functionally identical. What matters is helping clients understand whether their business gets named when someone asks ChatGPT or Perplexity for a recommendation, not debating acronyms.
Do I need different strategies for AEO, GEO, and LLMO?
No, these three frameworks are not separate strategies but concentric circles with massive overlap. Almost everything you do for AEO also helps with GEO, and almost everything you do for GEO also contributes to LLMO. The core tactics remain consistent: write clear answers to specific questions early in each section, use structured data and schema markup, build topical authority through depth, earn citations from authoritative sources, and keep content updated. A page built with these fundamentals performs across traditional search, featured snippets, and AI-generated answers simultaneously.
How is AEO different from traditional SEO?
The fundamental difference between AEO and SEO is the goal and how success is measured. SEO's goal is to drive qualified traffic, measured by rankings and click-through rates. AEO's goal is to increase visibility in AI answers, measured by mentions and citations rather than clicks. However, Ahrefs found that 76% of AI Overview citations pull from pages already ranking in Google's Top 10, which means traditional SEO fundamentals like crawlability, clean technical structure, backlinks, and E-E-A-T remain the foundation regardless of whether the end goal is a traditional click or an AI citation.