The language of AI discovery is evolving quickly. Marketers, product teams, and SEOs are experimenting with new labels—GEO, AEO, AIO, LLMO, and AI SEO—to describe how brands get found across AI assistants, large language models, and search. Here’s a concise guide you can share with your team.

TL;DR

GEO = Generative Engine Optimization; optimize for AI‑generated answer engines (e.g., Perplexity, Google AI Overviews).

AEO = Answer Engine Optimization; older umbrella for non‑traditional search that returns direct answers.

AIO = AI Optimization; broad governance of data and content for AI use.

LLMO = Large Language Model Optimization; make your brand quotable and fetchable by LLMs.

AI SEO = AI‑era Search Strategy; applying SEO thinking to AI surfaces (answers, chat, summaries).

None of these terms is a formal standard. Use the label that best fits your initiative and audience.

Why these names exist

Discovery has expanded beyond the ten blue links. People get answers from AI summaries, chat assistants, smart overviews, and aggregators. Teams coined new terms to signal scope and accountability: is the work about search engines, answer engines, AI governance, or model‑level visibility?

Working definitions

GEO — Generative Engine Optimization

Focus: Visibility within generative answer engines that synthesize web sources into a single response.

Targets: Perplexity, Google AI Overviews, Arc Search, Bing Copilot answers, Brave Summarizer.
Levers: Source eligibility, citation‑worthiness, crawlability, structured data, freshness, authority.
Outcome: Appear as a cited source or be the canonical reference in generated answers.

AEO — Answer Engine Optimization

Focus: Earning placement in answer‑first experiences beyond classic search.

Targets: Featured snippets, knowledge panels, voice assistants, zero‑click cards, Q&A modules.
Levers: Concise answer formatting, entity linking, FAQ markup, authoritative sourcing.
Outcome: Your answer is read, quoted, or surfaced directly to users.

AIO — AI Optimization

Focus: Broad readiness for AI consumption across product, data, and content.

Targets: Data pipelines, content governance, licensing, model access, retrieval systems.
Levers: High‑quality corpora, clear rights, embeddings/RAG, consistent schemas, safety reviews.
Outcome: Your information is reliably usable by AI systems and compliant with policy.

LLMO — Large Language Model Optimization

Focus: Make your brand and facts discoverable and quotable by LLMs specifically.

Targets: Model pretraining signals, retrieval indexes, tools/plugins, model cards and evals.
Levers: Canonical facts pages, unique datasets, well‑structured docs, machine‑readable attributions.
Outcome: Models cite or use your content as the source of truth.

AI SEO — AI‑era Search Strategy

Focus: Apply SEO discipline to a world of AI‑mediated search.

Targets: Traditional SERPs plus AI overviews, chat answers, summaries, and shopping cards.
Levers: Topic authority, content depth, entities, structured data, UX performance, E‑E‑A‑T.
Outcome: Sustainable visibility across both search and AI answer surfaces.

How they differ

Term	Primary scope	Main goal	Typical owner
GEO	Generative answer engines	Get cited or used as a source	SEO + Content + PR
AEO	Answer experiences (search/voice)	Be the direct answer	SEO + Content
AIO	Org‑wide AI readiness	Make data usable by AI	Product + Data + Legal
LLMO	LLMs and their toolchains	Be a trusted, retrievable fact	DevRel + Docs + SEO
AI SEO	Search + AI surfaces	Compound visibility and traffic	SEO

When to use which term

Pitching content teams: use GEO or AI SEO to motivate answer‑surface visibility and citations.
Aligning with product/data: use AIO to frame AI readiness, governance, and rights.
Talking to developer relations: use LLMO to focus on docs, tools, and model retrievability.
Explaining legacy concepts: use AEO when connecting to snippets/voice lineage.

Practical checklist

For GEO / AI SEO

Publish definitive, citation‑ready explainers and data‑backed pages.
Add schema (FAQ, HowTo, Dataset, Product) where truthful.
Use canonical, stable URLs; optimize titles for answer intent.
Keep facts fresh; update last‑modified and changelogs.
Attract links from expert and news domains.

For AIO / LLMO

Centralize a source‑of‑truth page for key facts and stats.
Provide machine‑readable artifacts (CSV/JSON) with clear licenses.
Document APIs and tools; enable retrieval with embeddings/RAG.
Track where models cite you; file feedback for misattributions.
Establish governance: quality thresholds, safety, and rights.

FAQ

Is GEO the same as AI SEO?

No. GEO is narrowly about generative answer engines; AI SEO applies SEO thinking across all AI‑mediated search surfaces, including classic SERPs.

Does AEO still matter?

Yes. Many AI answers are built on the same signals that power snippets, knowledge panels, and entity graphs. Structuring answers is still foundational.

What’s uniquely “LLMO” vs “AIO”?

AIO is organizational readiness for AI broadly. LLMO focuses on making your content discoverable by large language models—pretraining exposure, RAG inclusion, tools, and citations.

GEO, AEO, AIO, LLMO, and AI SEO: What They Mean—and How They Differ