GEO

LLM-Auszeichnung

LLM Citation is the technical mechanism by which a Large Language Model attributes specific generated text to a training document or retrieved data source. For brands to earn citations, content must be formatted (often via JSON-LD) so the model's attention mechanism recognizes it as the primary authority on a topic.

GEO
KI-Technologie
Technisches SEO

The Technical Foundation of AI Visibility

There's a critical difference between "training data" (background knowledge the LLM absorbed) and "cited sources" (active references it displays to users). When ChatGPT says "Nike makes shoes," that's general training data—no citation, no traffic. When it says "Nike released the Air Max DN on March 26, 2024" and links to the press release, that's a citation—you get the click. The technical key is structured data: JSON-LD schema tells the LLM's retrieval system exactly what information to extract and attribute. Without structured markup, your content becomes generic training fodder. With proper implementation, you become a cited authority that drives measurable traffic from AI interfaces.

General Mention vs. LLM Citation

Aspekt
Ohne
With LLM
Attribution
AI mentions brand from training data
AI cites specific source with link
Traffic Impact
Zero clicks - just brand awareness
Direct traffic from citation link
Content Format
Unstructured HTML text
Structured JSON-LD schema markup
Beispiel
"Nike makes athletic shoes" (generic)
"Nike Air Max DN - $160" [link] (specific)

Auswirkungen in der realen Welt

Vorher
Aktueller Ansatz
📋 Szenario

Product page has unstructured pricing information

⚙️ Was passiert

Perplexity.ai: "Prices vary, check their website"

📉
Wirtschaftliche Auswirkungen

User clicks competitor with clear pricing

Danach
Optimierte Lösung
📋 Szenario

Add JSON-LD Product schema with price + availability

⚙️ Was passiert

Perplexity.ai: "Product X is $99, in stock" [citation]

📈
Wirtschaftliche Auswirkungen

User clicks citation, high purchase intent

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