How are LLMO, AEO, and GEO related?

LLMO is the broader umbrella that contains both AEO (answer-engine-focused) and GEO (generative-search-focused). AEO is a subset of GEO, and GEO is a subset of LLMO. LLMO additionally covers direct LLM queries and AI agents that the narrower terms do not address.

LLMO vs SEO vs AEO vs GEO

Q: What is the difference between LLMO, SEO, AEO, and GEO?

SEO optimizes for search engine rankings (Google, Bing). AEO optimizes to become the direct answer in answer engines (voice assistants, featured snippets). GEO optimizes for visibility in generative search engines (ChatGPT, Perplexity). LLMO is the umbrella term that includes AEO and GEO and extends to all LLM interactions, including direct chat queries, RAG applications, and autonomous AI agents.

Q: Which one should I optimize for?

Optimizing for LLMO covers AEO and GEO as a side effect, because LLMO is a superset. Sites that only optimize for SEO will still rank in Google but may be invisible to ChatGPT, Claude, Gemini, and Perplexity. Start with LLMO if your audience uses AI tools to discover content.

LLMO, SEO, AEO, and GEO are four overlapping content-optimization disciplines. LLMO is the umbrella concept that includes AEO and GEO and extends to all LLM interactions, while SEO is the older sibling that targets search engines rather than AI systems.

Still pinning down the core term? Start with What is LLMO? — this page assumes you already have the definition.

What is the difference between LLMO, SEO, AEO, and GEO?

SEO (Search Engine Optimization, 1997-) — optimizes for ranking in search engine results (Google, Bing). Signals: backlinks, keywords, technical performance.
AEO (Answer Engine Optimization, 2018-) — optimizes to become the direct answer in answer engines (voice assistants, featured snippets). Signals: question-form headings, structured Q&A.
GEO (Generative Engine Optimization, 2023-) — academic framework for optimizing visibility in generative search engines (ChatGPT, Perplexity). Signals: statistics, citations, authority quotes.
LLMO (Large Language Model Optimization, 2024-) — umbrella discipline covering AEO + GEO + direct LLM queries + RAG + AI agents. Signals: clarity, structure, retrieval, authority, citation, coherence.

1997: SEO — Optimize for search engines
2018: AEO — Optimize for answer engines
2023: GEO — Optimize for generative engines
2024: LLMO — Optimize for all LLM interactions

Comparison table

	SEO	AEO	GEO	LLMO
Focus	Search rankings	AI answers	Generative search	All LLM interactions
Target	Google, Bing	Voice assistants, AI search	AI-powered search engines	ChatGPT, Claude, Gemini, Perplexity
Academic backing	Decades of research	Limited	Princeton (KDD 2024)	Emerging
Framework	Well-established	Informal	Research-focused	LLMO Framework (6 components)
Scope	Web search	Narrow (answers only)	Narrow (generative search)	Broad (all LLM contexts)

LLMO contains both AEO and GEO as subsets and extends beyond search to cover all contexts where LLMs interact with web content.

LLMO (all LLM interactions)
├── GEO (generative search engines)
│   └── AEO (answer-focused search)
└── Direct LLM queries (ChatGPT, Claude, etc.)
    └── RAG-based applications
    └── AI agents browsing the web

In one sentence: AEO ⊂ GEO ⊂ LLMO — every AEO win is a GEO win is an LLMO win, but not the other way around.

Which one should I optimize for?

Optimize for LLMO if you want to cover the broadest surface. LLMO is a superset, so its checklist covers AEO and GEO as a side effect. Sites that optimize for SEO alone may still rank in Google but be invisible to ChatGPT, Claude, Gemini, and Perplexity — which is increasingly where users start their queries.

Start here: LLMO Quickstart in 30 minutes covers the three essential files (robots.txt, llms.txt, JSON-LD) that move a site from invisible to AI-citable.

Working in local or map-search? LLMO vs GEO vs AEO for local business applies this same comparison to Google Business Profile, NAP entity resolution, and how each AI engine cites local data.