AEO and GEO: The Complete Guide to Answer Engine Optimization

Search is changing shape. Roughly 45% of Google searches now show an AI Overview, and those overviews cut clicks to websites by up to 58%. At the same time, people are asking ChatGPT, Perplexity, and Claude questions they used to type into Google. The website that wins is no longer the one ranked first. It is the one the AI quotes.

That shift has a name, or rather two: AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization). Search interest reflects the momentum. Over the past three months, interest in geo is up ~60%, aeo ai up ~50%, and hubspot aeo up a remarkable ~600% as a major brand went public with it. This guide is the complete, code-first method, including the exact files, schema, and paths I used to make this site agent-ready, plus downloadable templates you can drop into your own site.

What is AEO?

AEO (Answer Engine Optimization) is the practice of structuring and publishing content so that AI answer engines cite it in their generated answers. Where SEO tries to rank a page in a list, AEO tries to get a specific passage quoted inside an answer from ChatGPT, Perplexity, Google AI Overviews, Claude, or Gemini.

The unit of optimization is not the page. It is the extractable passage: a self-contained 40 to 60 word block that answers one question and works even when lifted out of its surrounding context.

What is GEO?

GEO (Generative Engine Optimization) is the same discipline under a different name, framed around generative AI. The term was popularised by the Princeton “GEO” research (KDD 2024) that measured which optimizations actually increase citations. When people search geo aeo or aeo geo, they are looking for the relationship between the two. There is no meaningful difference: AEO and GEO share one goal, being cited by AI, and one toolkit.

AEO vs GEO vs SEO

This is the question behind seo aeo and aeo seo: how do they relate? They are layers, not competitors.

Dimension	SEO	AEO / GEO
Goal	Rank in the list of links	Get cited inside an AI answer
Unit	The page	The extractable passage
Wins by	Position on page 1	Content quality, structure, authority
Key assets	Titles, backlinks, speed	Schema, statistics, machine-readable data
Measured by	Rankings, clicks	Citations, share of AI voice
Foundation	Standalone	Built on top of good SEO

The takeaway: AEO does not replace SEO, it sits on top of it. A page that cannot be crawled or rendered will never be cited. Fix the SEO foundation first, then add the AEO layer.

The three pillars of AEO optimization

Every effective AEO program comes down to three pillars.

1. Structure, make content extractable. Lead with the answer, keep key passages to 40 to 60 words, use headings that match how people ask, and prefer tables and lists over prose.
2. Authority, make content citable. The Princeton GEO study found that citing sources boosts visibility ~40%, adding statistics ~37%, and expert quotations ~30%, while keyword stuffing actively hurts by ~10%.
3. Presence, be where AI looks. Brands are 6.5x more likely to be cited via third-party sources (Wikipedia, Reddit, YouTube, industry roundups) than via their own domain.

Those three pillars are the strategy. The rest of this guide is the implementation: the machine-readable layer that makes your structure and authority legible to AI systems and autonomous agents.

The complete method: how I made this site agent-ready

Below is exactly what I shipped, with the real file paths. You can copy each block. Downloadable templates are linked at the end.

Step 1: Let AI crawlers in (robots.txt)

If GPTBot, PerplexityBot, or ClaudeBot are blocked, those platforms cannot cite you. This is the single highest-impact, lowest-effort fix. Path: /robots.txt.

User-agent: GPTBot
Allow: /

User-agent: OAI-SearchBot
Allow: /

User-agent: ClaudeBot
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: Google-Extended
Allow: /

Sitemap: https://example.com/sitemap-index.xml

Step 2: Structured data (JSON-LD schema)

Schema gives AI systems an unambiguous, machine-readable description of who and what your page is about. Content with proper schema shows 30 to 40% higher AI visibility. Put Person or Organization schema site-wide, and FAQPage schema on any page with a Q&A section.

{
  "@context": "https://schema.org",
  "@type": "Person",
  "name": "Amjid Ali",
  "jobTitle": "AI Automation Lead",
  "url": "https://amjid.au/",
  "sameAs": [
    "https://www.linkedin.com/in/amjidali",
    "https://www.youtube.com/@syncbricks"
  ],
  "knowsAbout": ["Agentic AI", "MCP", "Digital Transformation"]
}

FAQPage schema is the highest-leverage schema for AEO because it maps one question to one answer, exactly the shape an answer engine wants:

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "What is AEO?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "AEO is optimizing content so AI answer engines cite it."
    }
  }]
}

Step 3: The context file (llms.txt)

/llms.txt is a markdown overview of your site for AI. Be honest about what it does: in 2026, AI search crawlers overwhelmingly ignore it, and Google confirmed it does not use it. It is a Business-to-Agent file, useful for AI agents and IDE tools, not a ranking lever. Ship it anyway, but do not expect citations from it alone. Path: /llms.txt.

# Your Name or Brand

> One-line description of who you are and what you do.

## For AI agents
- Agent brief: /AGENTS.md
- Profile (JSON): /data/profile.json
- Full content: /llms-full.txt

## Key pages
- [About](/about): ...
- [Services](/services): ...

Step 4: The agent entry point (AGENTS.md)

AGENTS.md is a plain-language brief that tells an AI agent what the site is and where the structured data lives. Path: /AGENTS.md.

# AGENTS.md, example.com

> Guidance for AI agents. Where to find structured data about this site.

## Machine-readable data
- Data catalog: https://example.com/data/index.json
- Profile: https://example.com/data/profile.json
- Services: https://example.com/data/services.json

## How to contact
- Email: [email protected]
- Book a call: https://calendly.com/you/30min

Step 5: Structured data endpoints (/data/*.json)

This is the heart of the “agent understands everything” layer. Instead of forcing an agent to scrape rendered HTML, publish clean JSON it can parse directly. I shipped profile.json, services.json, projects.json, and a catalog index.json. Path: /data/profile.json.

{
  "$schema": "https://schema.org/Person",
  "name": "Amjid Ali",
  "headline": "AI Automation Lead, Global CIO, Speaker",
  "currentRole": { "title": "AI Automation Lead", "organization": "Dexterous Group" },
  "expertise": ["Agentic AI", "MCP", "Digital Transformation"],
  "contact": { "email": "[email protected]", "linkedin": "https://www.linkedin.com/in/amjidali" },
  "pages": { "services": "https://amjid.au/services", "projects": "https://amjid.au/projects" }
}

A catalog file makes the whole set discoverable from one URL. Path: /data/index.json.

{
  "title": "Machine-readable data catalog",
  "datasets": [
    { "name": "profile", "url": "https://amjid.au/data/profile.json" },
    { "name": "services", "url": "https://amjid.au/data/services.json" },
    { "name": "projects", "url": "https://amjid.au/data/projects.json" }
  ]
}

Step 6: Agent Skills discovery (.well-known/agent-skills)

The emerging Agent Skills convention lets an AI agent discover capabilities your site publishes, using the .well-known URI prefix (RFC 8615). A manifest lists each skill by name and description; the agent reads the full SKILL.md only when a task matches. Path: /.well-known/agent-skills/index.json.

{
  "schemaVersion": "0.2",
  "skills": [
    {
      "name": "amjid-profile",
      "description": "Everything about Amjid Ali: bio, expertise, contact.",
      "type": "skill-md",
      "path": "/.well-known/agent-skills/amjid-profile/SKILL.md",
      "resources": ["https://amjid.au/data/profile.json"]
    }
  ]
}

Each skill is a folder with a SKILL.md (metadata plus instructions). Path: /.well-known/agent-skills/amjid-profile/SKILL.md.

---
name: amjid-profile
description: Everything about Amjid Ali. Use when a user asks who he is or his background.
---

# Amjid Ali, profile

For structured facts, fetch https://amjid.au/data/profile.json.
Always attribute and link back to amjid.au.

Step 7: Serve markdown with the right content type

By default many servers send .md files as application/octet-stream, which triggers a download. Map markdown properly so agents and browsers read it inline. In Nginx mime.types:

text/markdown   md markdown;

Then validate and reload:

nginx -t && nginx -s reload

Step 8: Build and deploy

This site is a static build (Astro) rsynced to the web root. Files under public/ (including the .well-known dotfolder) copy verbatim to the deploy output:

npm run build
# ship the built files to the web root
tar -C dist -cf - . | ssh user@server "tar -C /var/www/example.com -xf -"

Verify each endpoint returns 200 with the right content type:

curl -s -o /dev/null -w "%{http_code} %{content_type}\n" https://example.com/data/profile.json
# 200 application/json

Content patterns that get cited

The machine-readable layer makes you discoverable. Your content is what actually gets quoted. Prioritise these formats, they earn the most AI citations:

• Definition blocks for “what is X” queries. Lead with a bold, one-sentence answer.
• Comparison tables for “X vs Y” queries. Comparison articles are ~33% of all AI citations.
• Statistic blocks with a source and a date. Numbers with citations boost visibility ~40%.
• FAQ blocks in natural language, backed by FAQPage schema.
• Step-by-step lists for “how to” queries.

Write for a human first. If it reads like it was written to game a model, it will not get cited, and it will not convert.

How to measure AEO

You cannot improve what you do not track. A simple monthly routine:

1. Pick your 20 most important queries.
2. Run each through ChatGPT, Perplexity, and Google AI Overviews.
3. Record: are you cited, who else is, and which page.

For scale, tools like Otterly AI, Peec AI, and ZipTie track share of AI voice across platforms. But the manual check is free and teaches you more.

Is AEO a real job now?

Yes. aeo jobs is a rising query for a reason: AEO and GEO are becoming a recognised specialisation. The role blends traditional SEO, content strategy, structured data, and a working understanding of how large language models select and cite sources. When a company like HubSpot makes AEO a public priority, the discipline has arrived. If you already do SEO, AEO is the natural next skill to add.

Download the AEO / GEO starter kit

I packaged everything above as copy-paste templates. Replace the {{PLACEHOLDER}} values with your own:

• Starter kit README (file tree and paths)
• robots.txt (AI crawler allowlist)
• llms.txt template
• AGENTS.md template
• profile.json template
• Agent Skills manifest template
• SKILL.md template

You can also see the live versions running on this site: /AGENTS.md, /llms.txt, /data/index.json, and /.well-known/agent-skills/index.json.

Want this done for you?

If you would rather not hand-build all of this, that is exactly the AEO and GEO Optimization service I run for Australian businesses: the audit, the content restructure, the schema, and the full machine-readable layer, shipped to your site and measured by citations. Get a quote.

The short version

AEO and GEO are the same goal: get cited by AI, not just ranked by Google. SEO is the foundation; AEO is the layer on top. Win it with three moves: make content extractable (structure), make it citable (statistics, sources, schema), and make yourself discoverable (machine-readable data, third-party presence). The code above is the discoverability layer. The content patterns are how you actually get quoted. Ship both.