Why there are four terms (and what they have in common)

The four terms emerged at different points and from different angles on the same underlying shift: search behavior is fragmenting across Google's ten blue links, AI Overviews, and conversational AI chatbots. SEO is the original discipline. GEO, AEO, and LLMO are 2024-2026 framings of the new layer.

All four share the same goal: be the source that buyers reach when they search. What changes is the surface they emphasize (Google rankings vs AI Overviews vs chatbot answers vs LLM citations specifically) and the optimization tactics weighted highest. The four rounds below clarify which framing fits your team.

The work, stage by stage

ROUND 1: DEFINITIONS

What each one actually means

SEO (Search Engine Optimization) is the original discipline. Optimize content, structure, and external signals so search engines (primarily Google) rank your pages well on the classic results page. The unit of value is a click from a results page. Decades of established practice.

GEO (Generative Engine Optimization) is the practice of structuring content, schema, and signals so generative AI engines like ChatGPT, Perplexity, Gemini, and Google AI Overviews cite your brand inside their answers. The unit of value shifts from a click to a citation. GEO covers the broadest portfolio: AEO-style answer optimization plus comparison content, alternatives pages, external citation work, and programmatic surfaces.

AEO (Answer Engine Optimization) focuses tightly on direct- answer formats: featured snippets, Google AI Overview extractions, ChatGPT one-line responses, and Perplexity answer cards. Emphasizes FAQ blocks, definition-led openers, structured Q/A content, and FAQPage schema. Mostly an on-page discipline.

LLMO (Large Language Model Optimization, also called LLM SEO) frames the same problem from the language-model angle. How do you become a source that LLMs reach for at inference time? Emphasizes content extractability, entity consistency across the web, and presence on the sources LLM retrieval reaches for (Reddit, YouTube, Stack Overflow, editorial heavyweights, Wikipedia).

ROUND 2: DIFFERENCES

How they actually differ

Side by side, the four disciplines differ on three dimensions:

  • Engines emphasized. SEO emphasizes Google's classic ranked results plus Bing. GEO covers ChatGPT, Perplexity, Gemini, AI Overviews, and broader generative engines. AEO emphasizes engines that surface direct answers (AI Overviews, ChatGPT, featured snippets). LLMO emphasizes language models specifically (ChatGPT, Claude, Gemini, Perplexity at inference time).
  • Unit of value. SEO: a click from a results page. GEO: a citation inside an AI-generated answer. AEO: appearing in the direct-answer box. LLMO: being named by an LLM during its inference response. Different outcomes, related tactics.
  • External signal weight. SEO weights links heavily. GEO and LLMO weight a broader set of external signals (Reddit, YouTube, editorial, Wikipedia) in addition to links. AEO mostly lives on-page, with external signals as a smaller factor.

The overlap is large. The same technical fundamentals (crawlability, schema, internal linking, quality content) feed all four. The same structural choices (definition-led openers, FAQ blocks, comparison tables) win across all four. The differences mostly emerge in which external signals you prioritize and which engines you measure success against.

ROUND 3: OVERLAP

Where they overlap (which is most of the work)

The honest answer to "SEO vs GEO vs AEO vs LLMO" is that 70-80 percent of the work overlaps. The same content and structure choices benefit all four:

  • Crawlable HTML and clean technical SEO are table stakes for every discipline. Block crawlers and you lose all four.
  • Definition-led first sentences under every H2 produce extractable passages for AEO, citations for GEO and LLMO, and topical clarity for SEO.
  • FAQ blocks with FAQPage schema win featured snippets (AEO), extractable Q/A formats (GEO and LLMO), and structured content (SEO).
  • Comparison tables are the most cited format in AI Overviews and ChatGPT (GEO and LLMO), rank well for "X vs Y" queries (SEO), and extract cleanly into direct answers (AEO).
  • Statistic-backed claims with named-source citations increase trust signals for SEO, citation likelihood for GEO and LLMO, and direct-answer candidacy for AEO.
  • Quality content and topical depth matter everywhere.

Where the disciplines diverge: SEO emphasizes link building harder than the others. GEO and LLMO weight Reddit, YouTube, and broader external citation work more. AEO de-emphasizes external signals in favor of on-page structure. The overlap is most of the work; the divergence is the last 20 percent.

ROUND 4: DECISION

Which should you optimize for first?

Use this framework:

  • Start with SEO if: Your category's buyer queries still resolve mostly through Google's classic results; you have an existing ranking position to defend; you have no presence in AI search at all.
  • Add AEO if: You're losing traffic to featured snippets and AI Overview boxes; your content is informational; you have the on-page capacity to ship structural changes (FAQ blocks, schema, definition-led openers).
  • Add GEO if: AI search is a meaningful share of your category's commercial queries; your competitors are being cited across ChatGPT and Perplexity and you aren't; you have content team capacity for broader content portfolio plus external signal work.
  • Add LLMO if: Your category is dominated by chatbot-driven discovery (developer tools, technical SaaS); your buyers explicitly mention learning about products through ChatGPT or Claude; LLM citations are a clear proxy for your downstream pipeline.

The honest answer for most teams: run them in sequence, not parallel. SEO fundamentals first because they're the foundation. Add AEO once SEO is steady. Add GEO once AEO is steady. LLMO is largely covered by the GEO work for most categories.

From RankAI

How RankAI treats the four disciplines

RankAI treats SEO, GEO, AEO, and LLMO as one execution discipline because the on-site work overlaps and the measurement is the same. The platform ships programmatic pages structured for all four (definition-led + FAQ blocks + comparison tables + schema), tracks citation share across the engines that emphasize each discipline, and triggers rewrites when expected lift doesn't materialize.

For deeper reading on each discipline, see the pillar guides for GEO, AEO, and LLM SEO.

Common SEO vs GEO vs AEO vs LLMO mistakes

  • Treating them as competing disciplines. They aren't. Optimizing one to the exclusion of the others typically caps your ceiling.
  • Getting stuck on terminology. Teams that argue framing in planning meetings ship less than teams that ship structured content. The names matter less than the work.
  • Abandoning SEO for GEO. AI engines weight classic SEO signals (rankings, links, schema) heavily, especially for AI Overviews and Gemini. Stripping out SEO discipline loses the foundation.
  • Buying specialized tools for each discipline. Most modern platforms cover multiple disciplines. Stacking single-purpose tools is usually more expensive than one integrated platform.
  • Measuring one discipline and ignoring the others. Citation share, search position, and traffic should all be tracked. Reporting on only one hides which interventions worked.