GEO, LLMO, AEO: the new SEO for AI engines and agents

Three acronyms, one revolution

Since the emergence of generative AIs that can answer questions directly without redirecting to websites, classical SEO alone is no longer sufficient. Three new disciplines have emerged:

• GEO (Generative Engine Optimization), optimizing to be cited by generative engines (Google AI Overviews, SearchGPT, Perplexity)
• LLMO (Large Language Model Optimization), optimizing your presence in LLM training corpora and responses
• AEO (Answer Engine Optimization), structuring content so answer engines (Perplexity, Bing Copilot, You.com) select it as an authoritative source

GEO, Generative Engine Optimization

Google AI Overviews, SearchGPT, and Perplexity generate synthetic responses from selected sources. GEO aims to be among those sources, and ideally to be named in the generated response.

Practices aligned with official guidance

Helpful and reliable content. Google recommends people-first content that adds original, accurate and well-sourced information. That quality can be reviewed. Citation frequency in a third-party model often cannot.

Accessible content. Important information should be available as text, open to crawling and connected through clear internal links. Precise headings, useful tables and self-contained passages also help human readers.

Real freshness. An updated date is useful only when the page has actually been reviewed. Changing a date without a substantive revision is not a proven quality signal.

Accurate structured data. Schema.org markup must match the visible page. It can help Google understand content, but Google requires no AI-specific markup and does not guarantee inclusion in an AI feature.

LLMO, Large Language Model Optimization

LLMs (GPT-4o, Gemini 2.0, Claude 3) were trained on massive text corpora. What appeared in those corpora directly influences their responses, including commercial recommendations.

How to optimize for LLMs

Publish citable sources. A page becomes more useful when it offers a precise definition, reproducible method, verifiable data or original synthesis. That still cannot guarantee memorization or citation by a model.

Earn legitimate mentions. Links and citations should come from useful work, not manufactured Wikipedia, GitHub or directory entries. A relevant independent mention is stronger evidence than a self-published profile.

The llms.txt file. This file can provide a machine-readable index to services that choose to consume it. Google says it has no positive or negative effect on visibility or ranking in Google Search.

AEO, Answer Engine Optimization

Perplexity, You.com, and Bing Copilot retrieve real-time web sources to generate their responses. AEO aims to be systematically selected and cited by these tools.

Key AEO factors

Direct topical relevance. Answer engines favor pages whose title precisely matches the query. A page titled exactly "What is agentic commerce?" will be selected more often than an article that covers the topic in passing.

Extractable passages. Short, self-contained paragraphs that answer a specific question in 2-3 sentences, comprehensible without the rest of the article.

Citing reliable sources. Citing NRF, Google, Shopify, or academic studies reinforces AEO credibility.

Practical application to agentic commerce

Discipline	Concrete action	Expected impact
SEO and GEO	Keep content crawlable, indexable and useful	Eligibility for classic results and AI features
SEO and GEO	Review the facts and show a real update date	More reliable information for readers
LLMO	Maintain /llms.txt for tools that consume it	Easier discovery outside Google, without a ranking promise
Authority	Publish citable research and earn independent mentions	Observable, verifiable authority signals
AEO	Write direct, self-contained and sourced answers	Passages that are easier to understand and reuse
Measurement	Track impressions, clicks and conversions in GSC and GA4	Measured impact instead of assumptions

Priority action plan for 2026

1. Add publication and update dates to all pages
2. Give each section a precise heading that accurately describes it
3. Keep only accurate, useful structured data
4. Publish /llms.txt listing main pages with descriptions
5. Earn independent editorial citations without manufacturing a Wikipedia presence
6. Keep public technical documentation factual, current, and linked from canonical pages
7. Structure product pages with schema.org Product + Offer + Review

Further reading

• Structured data for AI agents: schema.org and UCP
• Trust and proof: what AI agents verify before recommending
• Search, Agent, Training: how to control AI crawlers on your store
• llms.txt for e-commerce
• GEO optimization for e-commerce: practical guide