SEO is ranking on the results page. AEO is being the answer at the top of the page. GEO is being cited inside the AI's response. All three use mostly the same signals. The emphasis you choose depends on where your buyers actually search.
SEO. The original discipline.
Search Engine Optimization is ranking in traditional search engine results pages. Google, Bing, DuckDuckGo. The ten blue links. You optimize a page so it ranks for a keyword, the user clicks, they land on your site.
SEO is not being replaced by the newer disciplines. It is being expanded. Traditional SEO remains essential for organic rankings and still drives the majority of actionable search traffic in most verticals. The signals that matter: on-page content quality, backlinks, technical health (Core Web Vitals, mobile, HTTPS), semantic HTML, title and meta tags.
Where SEO alone falls short in 2026: when AI Overviews appear on a query, click-through rate for the top-ranking page drops from 7.3% to 1.6%, a 58% decline based on Ahrefs' analysis of 300,000 keywords. Even if you rank first, the user often reads the AI summary and never clicks. SEO still earns the impression. It increasingly loses the click.
AEO. Answer engine optimization.
Answer Engine Optimization is the practice of getting your content extracted as the direct answer in Google AI Overviews, Featured Snippets, People Also Ask boxes, and voice search results.
Featured snippet optimization is the highest-correlation path to AI Overview visibility. Pages previously selected for featured snippets are cited in AI Overviews at roughly twice the rate of non-snippet pages, with 68% of AI Overviews citing the source that holds the featured snippet for that query. Translation: snippet wins stack directly into AI Overview citations.
AEO coverage has exploded in the last year. AI Overview appearances grew 58% year-over-year from February 2025 to 2026, making AEO increasingly non-optional for any business whose buyers search Google.
The signals that matter for AEO:
- Direct answer in the first 50 to 60 words of a section, under a question-shaped heading
- FAQPage schema
- Short, scannable passages (134 to 167 words per self-contained chunk)
- Clear question-to-answer heading patterns
- Strong E-E-A-T signals (named author, visible publication date, citations)
GEO. Generative engine optimization.
Generative Engine Optimization is being cited, mentioned, or recommended inside AI-generated responses from systems like ChatGPT, Perplexity, Claude, Gemini, DeepSeek, Grok and Copilot.
The difference between AEO and GEO is where the citation surfaces. AEO optimizes for being the answer in featured snippets, People Also Ask and AI Overviews. GEO optimizes for being cited inside generative responses across the broader LLM ecosystem, though the fundamentals overlap heavily.
The signals that matter for GEO:
- Entity presence across the open web (Wikipedia, Wikidata, Crunchbase, directories, press)
- Content that AI can lift verbatim as a cited passage
- Visibility in the specific index each AI reads from (Bing for ChatGPT, Brave for Claude, real-time crawl for Perplexity)
- Freshness, author authority, structured data
- Measured as citation share of voice across a defined prompt set, not keyword rank
LLMO. The entity layer.
LLMO (Large Language Model Optimization) is sometimes used as a synonym for GEO, but there is a useful distinction. LLMO targets trained LLM knowledge, not real-time retrieval, by influencing the associations language models build during training. It is the practice of building enough trusted, consistent presence that a language model names you from its own memory when prompted, without looking you up.
Where GEO focuses on getting cited in individual live responses, LLMO focuses on being correctly identified as a trusted entity when the model is NOT doing live retrieval.
In practice: if you ask Perplexity "best creator platform agency," GEO work is what gets you cited in the retrieved sources. LLMO work is what gets you named by Claude when asked the same question from training data without web search enabled.
Wikipedia is the single highest-leverage LLMO investment. LLMs train heavily on Wikipedia entity data, and for organizational and personal entities Wikipedia is often the single largest authoritative source in training data. Wikipedia gets special treatment because it is treated as neutral (not self-promotional), community edited, explicitly included in LLM training with high weight, and indirectly feeds the knowledge graphs that feed AI models.
The signal overlap
The useful truth: AEO, GEO and LLMO all build on SEO. They focus on different things, but they share most of the underlying work. Clean schema helps all four. Answer-first content helps all four. Entity consistency helps all four. Technical SEO (fast load, mobile, HTTPS, proper markup) helps all four.
Build one excellent, well-structured, well-sourced asset and you are doing most of SEO, AEO, GEO and LLMO at the same time. The question is which emphasis matters most to your category.
Which to prioritize
The practical sequence most experienced practitioners now recommend for B2B SaaS and services businesses is foundation first, then extraction, then citation. Build the AI SEO foundation first when the search base is weak, then build AEO into the content structure, then invest in GEO to earn citations across AI engines.
Lead with AEO when:
- Your content relies on capturing direct, question-based searches
- You run a local or service business answering specific queries
- Your buyers Google things like "best X in Phoenix" or "how do I fix Y"
Lead with GEO when:
- Your strategy depends on depth and credibility
- You run a SaaS, B2B or thought-leadership heavy business
- Your buyers research in ChatGPT, Claude, Perplexity before they Google anything
Lead with LLMO when:
- Brand equity and expert positioning drive your strategy
- You want to be the named entity in your category when AI answers from training data
- You have the long runway for entity-building (Wikipedia, Wikidata, academic references)
Lead with SEO when:
- You still need traditional organic traffic for conversions
- Your category has not yet been eaten by AI Overviews
- You have an existing content library that just needs technical and semantic upgrades
What NOT to do
Do not pick just one. The acronyms are not competing strategies. They are different emphases on the same underlying work. Picking "we only do GEO" is like picking "we only tune the carburetor." You still need the whole engine running.
Do not assume AI will replace Google overnight. AI Overview coverage is growing fast, but it still leaves most queries without an AI summary. Traditional organic rankings still drive the majority of commercial search traffic in most B2B categories.
Do not optimize for GEO at the cost of SEO. Pages that rank well on Google are more likely to be cited by AI. The two work together.
Do not treat these as separate workstreams. One page, structured correctly, hits all four. Separate "SEO team" and "AEO team" and "GEO team" organizational structures are a tell that the leadership does not understand the overlap.
The practical starting point
If you are starting from zero and want to cover all four with minimum effort:
- Audit your top 10 pages for answer-first format. Each H2 should be a question. The first paragraph underneath should be the direct 60-word answer. Covers SEO (passage ranking), AEO (snippet selection) and GEO (citation extraction) in one move.
- Add FAQPage schema to those 10 pages. It is the one schema type that measurably lifts citation rates on every AI assistant.
- Add visible named author bylines with linked bios, publication dates and external citations. This is the E-E-A-T stack. AEO, GEO and LLMO all weight it heavily.
- Build one entity-authority asset. A Wikipedia mention, a Wikidata entry, a Crunchbase profile. This moves the needle for LLMO specifically.
- Verify your site in Bing Webmaster Tools and Brave Search Webmaster Tools. Submit sitemaps. This is table stakes for GEO on ChatGPT (Bing) and Claude (Brave).
Do those five things and you have covered 80% of what SEO, AEO, GEO and LLMO require. The remaining 20% is category-specific optimization and ongoing content production.
What this does not cover
This article defines the acronyms and the overlap. It does not cover the specific tactics for each AI assistant (that is in How to Rank in ChatGPT, Claude, Perplexity and Gemini), the terminology (that is in the AI SEO Glossary), or the full practitioner service.
For the full service, see our AI SEO page.