The 5 Pillars of Technical Implementation
🎯 Learning Objective
You will know the 5 technical pillars of GEO, be able to implement each pillar practically, and understand what files, code structures and configurations need to be created.
Lesson 3.1: Pillar 1 — Crawler Control (robots.txt for AI)
The robots.txt has been the standard way to tell web crawlers which areas they may visit for decades. What has changed: A whole new generation of AI crawlers must be explicitly addressed.
The Most Important AI Crawlers
| Crawler | Operator | Function |
|---|---|---|
GPTBot | OpenAI | Training + web browsing for ChatGPT |
ChatGPT-User | OpenAI | Real-time browsing in chat sessions |
ClaudeBot | Anthropic | Claude training + browsing |
PerplexityBot | Perplexity | Real-time crawling for search queries |
Google-Extended | Gemini training | |
Meta-ExternalAgent | Meta | Meta AI training |
Best Practice — robots.txt Template
# AI Engine Crawlers — Allowed
User-agent: GPTBot
Allow: /
User-agent: ChatGPT-User
Allow: /
User-agent: ClaudeBot
Allow: /
User-agent: PerplexityBot
Allow: /
User-agent: Google-Extended
Allow: /
# Sitemap: https://example.com/sitemap.xml
# llms.txt: https://example.com/llms.txt
The 3 Most Common Mistakes
- 404 on robots.txt: AI crawlers crawl conservatively → less data, fewer citations.
- Global
Disallow: /: Blocks everything, often a relic from development. - Network-level bot protection: Cloudflare/Akamai block bots before they reach the robots.txt.
Practice Tip
Test with curl -A "GPTBot/1.2" -I https://example.com/ — a 403 Forbidden means: network is blocking the bot.
Lesson 3.2: Pillar 2 — llms.txt — The AI Business Card
The /llms.txt is a proposed standard (llmstxt.org, Jeremy Howard, fast.ai). It provides language models with a high-density fact briefing — like a sitemap.xml for search engines, but for LLMs.
Structure of a Professional llms.txt
# Brand Name
> One-sentence mission with maximum semantic density.
## About Us
Short profile: founding year, location, industry, team size.
## Products / Services
- **Produkt A**: Fact-based description, USP, price range
- **Produkt B**: Technical specifications, results
## Expertise & Credentials
ISO 9001, awards, reference clients
## FAQ
- **Was kostet [Produkt]?** From €X/month.
- **Who is it suitable for?** [Specific target audience].
## Contact
Website, email, location, management
The 4 Golden Rules
- Fact density over marketing speak. „120 employees at 3 locations" statt „a dynamic team".
- Citable sentences. Every sentence must work 1:1 in an AI answer.
- Consistency with the website. Contradictions → AI trusts no source.
- Maximum 4,000 tokens (approx. 2-3 pages).
Lesson 3.3: Pillar 3 — Structured Data (JSON-LD Schema)
JSON-LD is a W3C standard and the preferred format for machine-readable, structured data. It provides an unambiguous "translation" of the page contents.
Analogy: HTML without JSON-LD is like a book without a table of contents. The information is in there somewhere, but you have to read everything.
Required Schemas
| Schema Type | Purpose | Priority |
|---|---|---|
Organization | Who is the company? | 🔴 Required |
WebSite | What is the website? | 🔴 Required |
BreadcrumbList | Page structure | 🔴 Required |
FAQPage | Common questions | 🔴 Required |
Person | Experts | 🟡 High |
Product / Service | Offerings | 🟡 High |
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Beispiel GmbH",
"url": "https://beispiel.de",
"foundingDate": "2005-03-15",
"numberOfEmployees": { "@type": "QuantitativeValue", "value": 120 },
"address": {
"@type": "PostalAddress",
"addressLocality": "Düsseldorf",
"addressCountry": "DE"
},
"sameAs": [
"https://linkedin.com/company/beispiel",
"https://de.wikipedia.org/wiki/Beispiel_GmbH"
]
}
Lesson 3.4: Pillar 4 — Visual GEO (Optimizing Images for AI)
Modern AI models are multimodal: GPT-4o, Gemini and Claude can understand image content. Alt texts are therefore an active AI signal.
The Alt-Text Formula
[Subject] + [Context] + [Material/Property] + [Brand]
| Alt-Text | Quality |
|---|---|
"Serum" | ❌ Useless |
"Vitamin C Serum Flasche" | ⚠️ Better |
"Vitamin C Glow Serum, 30ml Glasflakon mit Pipette, MIRI Cosmetics" | ✅ Excellent |
Lesson 3.5: Pillar 5 — Answer Density
AI agents preferentially extract answers from the first 50–100 words after the H1. Placing empty marketing phrases here wastes the most valuable citable space.
Bad Example (80% of all websites)
„Welcome to Example Inc. We are a dynamic company that has been working with passion and innovation for many years..."
Good Example (Answer Density optimized)
„Beispiel GmbH ist der führende DACH-Anbieter für industrielle Filtertechnik seit 2005. 120 employees at 3 locations, ISO 9001 und ISO 14001 zertifiziert. HEPA-14-Filter mit 99,995% Abscheidegrad."
Practice Exercise
Open any company website. Copy the first 100 words after the H1. How many citable facts can you find? Write an optimized version.
📝 Quiz: Module 3
Test your understanding — 5 questions, 70% to pass.
Question 1: Was passiert, wenn robots.txt eine 404 zurückgibt?
Question 2: Was sind die 4 goldenen Regeln für eine effektive llms.txt?
Question 3: Wie lautet die Alt-Text-Formel für Visual GEO?
Question 4: Welche JSON-LD-Schema-Typen sind Pflicht?
Question 5: Warum sind die ersten 100 Wörter nach der H1 so wichtig?
About the Author
Sascha Deforth — GEO Practitioner and Founder of TrueSource AI. Specialized in AI Visibility Optimization with 200+ audits completed. → LinkedIn