What is AEO? Answer Engine Optimization Explained

AEO is the practice of optimizing your site to be quoted by AI engines (ChatGPT, Perplexity, Gemini, and Claude) when they answer user queries. It extends SEO into the era when traffic increasingly bypasses the results page and arrives as a citation inside an AI-generated answer.

I am Ignacio (IGNAX), Spain-born and Paraguay-based, a solo full-stack developer and SEO/AEO specialist. I run this exact playbook on ignax.dev itself; the live experiment is documented in the growth engine case study. The service page is SEO + AEO setup.

What is AEO, in one sentence?

AEO is making your site readable, indexable, and citable by large language models so that when a user asks an AI engine a question your domain answers well, your URL is the source it quotes.

What is the AEO checklist (the short version)?

If you implement nothing else, ship these six in order:

1. Quick Answer block: 40 to 60 words in the first 200 pixels of every key page, stand-alone, no anaphora, leading with the direct factual answer.
2. FAQPage JSON-LD schema: 5 to 8 questions per page, each answer ≤80 words, valid in Google's Rich Results Test.
3. All H2s phrased as questions: humans and AI engines both parse Q-as-heading structure efficiently.
4. llms.txt and llms-full.txt: at the root of your domain, listing your URLs with one-line descriptions.
5. IndexNow integration: ping Bing, Yandex, and aggregators on every publish or update.
6. Person + Organization schema with sameAs: verified entity links to your LinkedIn, GitHub, X, and any authoritative profile.

That is the minimum AEO setup. Everything else compounds, but those six are non-negotiable. For the full implementation guide see how to get cited by ChatGPT.

How does AEO differ from SEO?

The two practices overlap on infrastructure and diverge on content.

Dimension	SEO	AEO
Goal	Rank on results page	Get quoted in AI answer
Primary surface	google.com, bing.com	chatgpt.com, perplexity.ai, gemini.google.com, claude.ai
Content priority	Keyword targeting + depth	Direct answers + citation-worthy facts
Schema focus	Article, BreadcrumbList	FAQPage, Service, Person + sameAs
Discoverability layer	sitemap.xml, robots.txt	sitemap.xml + llms.txt + llms-full.txt
Update signal	GSC URL Inspection	IndexNow + GSC + Bing
Measurement	Rankings, CTR, GSC clicks	AI-engine citation probe (manual)

The site that wins at AEO usually also wins at SEO. The reverse is not always true, sites ranking well on Google may not appear in AI citations because their content is depth-first rather than answer-first.

Why does AEO matter in 2026?

Three forces shape the urgency. ChatGPT, Perplexity, Gemini, and Claude now serve answers to a meaningful share of buyer-intent searches between them. AI Overviews appear on a growing percentage of Google searches, with a measurable click-through reduction on the underlying organic results when the Overview answers the query. And the AI engines cite specific domains by name: Perplexity citations are first-class UI, ChatGPT cites with link previews, and Gemini cites in AI Overviews.

The traffic that used to come as a top-three organic click is now arriving as a citation inside an AI answer. If your site is not citable, that traffic does not arrive at all. AEO is how you stay visible as the surface shifts.

What is a Quick Answer block?

A Quick Answer block is a 40 to 60 word factual paragraph at the top of every key page, written as a stand-alone direct answer to the page's target query. It is the highest-leverage AEO element, AI engines lift Quick Answers verbatim into their responses far more often than they lift deeper paragraphs.

Rules I follow on every page I ship:

• 150 to 450 characters, which is roughly 40 to 60 words.
• No anaphora. Do not start with "This article…" or "Here is…". Lead with the topic itself: "AEO is…" or "A SaaS MVP costs…".
• Stand-alone factual answer. If the AI engine quotes just this paragraph. The user has the answer.
• No promotional language. "I'm the best" is not citable. "AEO is X" is.
• In the first 200 pixels of the rendered page, above any decorative content.

The SvelteKit content registry approach I ship encodes the Quick Answer in front-matter so the schema and the visible block stay in sync. Zod validates the 150 to 450 character range on every build.

What is llms.txt?

llms.txt is a proposed standard for declaring which URLs on your domain are worth reading for an LLM, with a short description per URL. It sits at the root of your domain, https://yourdomain.com/llms.txt: and acts as a curated table of contents for AI crawlers.

The format is intentionally simple, a markdown file with H1 (site name), an intro paragraph, then sections of links with one-line descriptions:

# Ignax.dev

> Solo full-stack developer and SEO/AEO specialist. SaaS MVPs, RAG chatbots, multilingual SEO.

## Services

- [SaaS MVP Development](https://ignax.dev/services/saas-mvp): Build production SaaS MVPs in 2–6 weeks
- [SEO + AEO Setup](https://ignax.dev/services/seo-aeo-setup): Get cited by AI engines

## Articles

- [What is AEO?](https://ignax.dev/articles/what-is-aeo): Answer Engine Optimization explained

For the full mechanics see llms.txt explained. The proposed spec lives at llmstxt.org and is the closest thing the AEO community has to a shared standard.

What schema do AI engines look for?

The schema types that move the needle for AEO citation:

• FAQPage: the highest-leverage schema for AI citation. AI engines lift FAQ answers into their responses verbatim when the question matches the user query.
• Article with author, datePublished, and datemodified: establishes freshness and authorship.
• Service: when offering a commercial service. This clarifies the offering, the provider, and the area served.
• Person with full sameAs array: links your author entity to LinkedIn, GitHub, X, Crunchbase, ORCID, or any reputable identity provider.
• Organization with sameAs: same logic at the company level.
• BreadcrumbList: improves crawl understanding of the site hierarchy.

Validate every JSON-LD block in the Google Rich Results Test and the Schema.org validator before declaring it shipped. The Schema.org full type list is the canonical reference.

How do I measure AEO performance?

Three signals, in priority order:

1. AI-engine citation count. Manually probe 10 to 20 target prompts across ChatGPT, Perplexity, Gemini, and Claude. Log every citation. This is the only direct AEO metric and it is unavoidably manual today: no public API exposes it.
2. URLs indexed in Google Search Console and Bing Webmaster Tools. The floor metric: if you are not indexed, you cannot be cited.
3. Ranking and CTR on buyer-intent queries in GSC. The leading indicator: sites that rank well on Google tend to be cited later in AI engines.

I baseline these on Day 1 of an engagement, share the dashboard, and update the growth engine case study monthly with real numbers. No fake metrics.

What does AEO setup cost?

For the full pricing breakdown see what does AEO setup cost. The short version: $1,800 to $4,500 USD for a 5 to 10 day engagement, depending on whether the site is single-language or bilingual and whether a full content sweep is in scope.

What references should I read?

• Google Search Central documentation; the canonical SEO reference, foundational for AEO too.
• Schema.org full type list: every JSON-LD type, in one place.
• llmstxt.org; the proposed llms.txt specification.
• OpenAI documentation on web browsing and citations: what ChatGPT-side citation actually looks like under the hood.

That is AEO in one explainer. The execution work, schema, llms.txt, IndexNow, Quick Answers, FAQ blocks, is mechanical once you have the pattern. For the implementation playbook see how to get cited by ChatGPT next.

Ready to ship AEO on your site? Email hello@ignax.dev with your URL. I run the crawl before our call so we do not waste the first 20 minutes on boilerplate.