LLM Search Strategy Framework

This framework is used by LLM Strategist professionals. Learn more about the LLM Strategist role.

LLM Search Strategy Framework

The LLM Search Strategy Framework is a 7-step methodology for optimizing brand visibility in AI answer engines. This framework is used by LLM Strategists to systematically improve citation rates, retrieval surface area, and entity alignment.

Step 1: Entity Grounding

Input: Brand name, products, services, key attributes

Process: Map brand entities to structured data schemas (Organization, Product, Service, Person)

Output: JSON-LD schemas that enable accurate entity recognition

Metrics: Schema validation rate, entity recognition accuracy

Step 2: Structured Data Execution

Input: Entity mappings from Step 1

Process: Implement JSON-LD schemas across key brand pages

Output: Machine-readable structured data on all critical pages

Metrics: Pages with valid structured data, schema coverage percentage

Step 3: Canonical Control

Input: All brand URLs and content variations

Process: Establish canonical URLs for each entity, implement canonical tags

Output: Clear authoritative sources for each brand entity

Metrics: Canonical citation accuracy, duplicate content reduction

Step 4: Citation Seeding

Input: Key brand facts, product information, service descriptions

Process: Structure content to make facts easily extractable by AI systems

Output: Content optimized for AI extraction and citation

Metrics: Citation rate, attribution accuracy

Step 5: Retrieval Optimization

Input: Content structure, entity alignment, canonical control

Process: Optimize content hierarchy and information architecture for AI discovery

Output: Expanded retrieval surface area

Metrics: Number of brand entities discoverable by AI systems

Step 6: Testing and Validation

Input: Implemented structured data and content

Process: Test queries in ChatGPT, Claude, Perplexity, Google AI Overviews

Output: Citation reports, retrieval accuracy assessments

Metrics: Citation rate, retrieval accuracy, entity alignment score

Step 7: Iteration and Optimization

Input: Test results, citation reports, metrics

Process: Refine structured data, content structure, canonical control based on results

Output: Improved citation rates and retrieval accuracy

Metrics: Citation rate improvement, retrieval surface area expansion

Metrics and Evaluation

LLM Strategists measure success using three primary metrics:

• Citation Rate: How often AI systems cite your brand when users ask relevant questions. Measured as citations per 100 relevant queries.
• Retrieval Surface Area: How many brand entities AI systems can find and cite. Measured as number of distinct entities discoverable by AI systems.
• Entity Alignment: How accurately AI systems associate your brand with intended topics and services. Measured as percentage of correct entity associations.

Additional metrics include attribution accuracy (are citations correct?), canonical citation rate (are AI systems citing authoritative sources?), and retrieval latency (how quickly do AI systems find brand information?).

LLM Strategist role overview