How LLM Strategists Influence Retrieval and Citations
LLM Strategist professionals use these mechanisms to influence retrieval and citations. Learn more about the LLM Strategist role.
How LLM Strategists Influence Retrieval and Citations
LLM Strategists influence AI systems through four primary mechanisms. Each mechanism changes how AI systems find, understand, and cite brand information.
1. Entity Grounding
What it is: Ensuring AI systems correctly identify and classify brand entities using structured data.
How it works: LLM Strategists implement JSON-LD schemas (Organization, Product, Service) that provide clear, machine-readable information about brand entities. When AI systems process web content, they use this structured data to accurately identify and classify entities.
Impact: AI systems correctly recognize your brand as an organization, your products as products, and your services as services. This enables accurate entity associations and citations.
Example: A brand implements Organization schema with name, logo, description, and contact information. When users ask "What is [brand]?", AI systems retrieve the structured Organization data and cite it accurately.
2. Structured Data Execution
What it is: Implementing JSON-LD schemas that provide clear, machine-readable information about products, services, and organizations.
How it works: LLM Strategists add structured data to key brand pages, making information easily extractable by AI systems. This includes product details, service descriptions, organization information, and key facts.
Impact: AI systems can quickly extract accurate information without parsing unstructured content. This improves citation accuracy and reduces errors.
Example: A product page includes Product schema with name, description, price, availability, and reviews. When users ask about the product, AI systems extract this structured data and cite it correctly.
3. Canonical Control
What it is: Managing which URLs AI systems treat as authoritative sources through proper canonical tags and internal linking.
How it works: LLM Strategists establish canonical URLs for each brand entity and implement canonical tags. They also use internal linking to reinforce which pages are authoritative sources.
Impact: AI systems cite the correct authoritative sources, not duplicate or non-canonical versions. This improves citation accuracy and brand consistency.
Example: A brand has multiple URLs for the same product (with/without tracking parameters, different locales). Canonical tags ensure AI systems cite the main product page, not variations.
4. Citation Seeding
What it is: Creating content structures that make it easy for AI systems to extract and cite accurate information.
How it works: LLM Strategists structure content with clear hierarchies, factual statements, and extractable facts. They use consistent formatting, clear headings, and structured lists that AI systems can easily parse.
Impact: AI systems can quickly find and extract key facts, leading to more accurate citations and better brand representation.
Example: A service page uses clear H2 headings for each service feature, bullet points for key facts, and structured tables for comparisons. AI systems extract these structured elements and cite them accurately.
Signals that Change
When LLM Strategists implement these mechanisms, measurable signals change:
Before Implementation
- Citation rate: 0-5 citations per 100 relevant queries
- Retrieval surface area: 1-2 brand entities discoverable
- Entity alignment: 40-60% correct associations
- Attribution accuracy: 50-70% correct citations
After Implementation (90 days)
- Citation rate: 15-25 citations per 100 relevant queries
- Retrieval surface area: 5-8 brand entities discoverable
- Entity alignment: 80-90% correct associations
- Attribution accuracy: 85-95% correct citations
Note: Results vary based on brand size, industry, and implementation quality. These are typical ranges observed across multiple implementations.
LLM Strategist role overview