What is the difference between AEO and GEO?

AEO is usually used for answer visibility across AI and search answer surfaces. GEO comes from generative engine optimization and focuses on visibility in generative search responses.

Is LLMO a standard term?

LLMO is used by some teams to mean large language model optimization, but the term is less standardized than SEO and less tied to one measurement framework than AEO or GEO.

Which term should my team use?

Use SEO for search result visibility, AEO for answer visibility, GEO for generative search strategy, and LLMO only when you are specifically discussing LLM retrieval or model-facing content.

AEO vs GEO vs SEO vs LLMO: What Each Term Means

The language around AI search is messy. Teams use AEO, GEO, SEO, LLMO, AI SEO, answer engine optimization, and generative engine optimization to describe overlapping work.

That creates a planning problem. If a founder says "we need GEO," a content lead hears "write AI search content," an SEO lead hears "update structured data," and a product marketer hears "monitor competitors in ChatGPT." Those are related, but they are not the same job.

This guide gives each term a practical boundary so your team can decide what to measure and what to ship.

Quick Definitions

Term	Full name	Best used for	Primary measurement
SEO	Search Engine Optimization	Visibility in search result pages	rankings, impressions, clicks
AEO	Answer Engine Optimization	Visibility inside generated answers	mentions, citations, competitors
GEO	Generative Engine Optimization	Strategy for generative search responses	source visibility, answer inclusion
LLMO	Large Language Model Optimization	Model-facing retrieval and extraction readiness	crawlability, extractability, entity clarity

The terms overlap because the same page can support all four. A clear product page can rank in Google, appear in Google AI features, be cited by Perplexity, and be easier for a retrieval system to summarize.

The difference is the scoreboard.

SEO: Search Result Visibility

SEO is the mature discipline. It focuses on making pages crawlable, indexable, useful, and competitive in search results. Google Search Central describes Search as a process of crawling, indexing, and serving pages, and Search Console remains the main tool for monitoring Google Search performance.

SEO outputs include technical fixes, page updates, metadata, internal links, structured data, content refreshes, and authority building. The metrics are impressions, clicks, rankings, click-through rate, index coverage, and organic conversions.

SEO is still the base layer. If a page cannot be crawled, loaded, or understood, it will struggle in both traditional and AI search experiences.

AEO: Answer Visibility

AEO focuses on whether a brand appears in the answer itself. It asks:

Did the answer mention our brand?
Did it mention competitors?
Did it cite our owned pages or third-party proof?
Did the answer frame us as relevant, absent, risky, or recommended?

This is the layer AEO Table measures. A Task defines the buyer questions, channels, market, language, and competitors. Each Run captures a snapshot of how answer engines respond. Reports preserve mentions, citations, and evidence.

If SEO is about being a result, AEO is about being part of the answer.

GEO: Generative Search Strategy

GEO, or Generative Engine Optimization, comes from the research framing in the GEO paper. It focuses on visibility in generative engines that synthesize answers from sources.

In practice, GEO is useful as a strategy term. It covers content structure, authoritative claims, source clarity, citations, entity signals, and domain-specific optimization. A GEO playbook might include updating pages with clearer claims, adding source-backed statistics, building third-party mentions, and improving retrieval-friendly structure.

If your team is discussing the strategic shift from ranking pages to being included in generated answers, GEO is a good umbrella term. For execution and reporting, you still need AEO-style metrics.

For a deeper strategy view, read the Generative Engine Optimization playbook.

LLMO: Model-Facing Readiness

LLMO usually means Large Language Model Optimization. It is less standardized, but the useful version is narrow: make your public content easier for LLM-powered retrieval and answer systems to access, parse, and summarize.

That includes:

Server-rendered or otherwise crawlable content.
Clear headings that match user questions.
Concise definitions near the top of the page.
Structured data where it matches the page content.
Accurate robots and sitemap configuration.
Entity consistency across brand names, domains, and third-party profiles.

LLMO can become vague fast. Use it when you are discussing model-facing retrieval and extraction. Use AEO when you are measuring the resulting answer visibility.

Where The Terms Overlap

The same content update can serve all four disciplines.

For example, a page titled "How to monitor AI search visibility" can:

Improve SEO if it matches a search query and earns organic traffic.
Improve AEO if answer engines mention and cite it.
Support GEO if it becomes source material in generative answers.
Support LLMO if it is crawlable, structured, and easy to extract.

That overlap is good. The mistake is using one term to hide missing measurement.

If a team says it is doing AEO but only tracks Google rankings, the answer layer is unmeasured. If a team says it is doing GEO but ships thin pages with no source quality, the content layer is weak. If a team says it is doing LLMO but cannot say which buyer questions matter, the work is too abstract.

Which Workflow Should You Use?

Use this decision rule:

Use SEO when the goal is organic search performance.
Use AEO when the goal is brand visibility in AI answers.
Use GEO when the goal is a broader generative search content strategy.
Use LLMO when the goal is crawlability, parsing, and extraction readiness for LLM-powered systems.

For most B2B teams, the practical operating model is:

Keep SEO fundamentals healthy.
Build an AEO baseline across buyer questions.
Use GEO strategy to decide what source material to create.
Apply LLMO checks to make those pages accessible and extractable.

The AI search monitoring use case ties these pieces together.

A Simple Example

Imagine a SaaS company that wants visibility for "best compliance automation tools."

The SEO task is to build and maintain a page that can rank for compliance automation queries.

The AEO task is to monitor whether ChatGPT, Google AI Overview, and Perplexity mention the brand for compliance automation and alternative queries.

The GEO task is to publish credible source material: comparison criteria, proof, security documentation, customer examples, and third-party references.

The LLMO task is to make those pages easy to access and parse: clear H2s, concise answers, structured data, current dates, and clean internal links.

One content program. Four lenses.

The Bottom Line

Do not get stuck arguing terminology. Pick the term based on the measurement you need.

If your board asks whether AI answers mention your brand, that is AEO. If your content team asks how to become better source material for generated answers, that is GEO. If your technical team asks whether AI systems can crawl and extract your pages, that is LLMO. If your growth team asks about Google clicks, that is SEO.

The best teams connect all four, then report them separately.

Start a free AEO Table baseline to see where your brand appears across AI answer engines today.