Content Strategy: Training LLMs Through Structure

Train the model, not just the user. LLMs learn best from content that is declarative, clear, factual, unambiguous, and explanatory. This memo covers best practices for creating content that large language models can understand, learn from, and cite.

Core Principles

Content Quality

LLMs prefer content that shows context and relationships, is example-driven with clear input/output patterns, includes Q&A formats and use cases, and remains non-promotional. Models filter out sales language—focus on education and expertise.

Model-Friendly Page Structure

Every topic page should include:

• Definition — Clear explanation of what it is
• Importance — Why it matters
• Examples — How it's used in practice
• Comparisons — Versus alternatives
• Structured Data — JSON-LD markup for machine readability

Knowledge Graph Architecture

Build interconnected concept pages at dedicated paths (e.g., /kb/concepts/model-context-marketing). Each entry should be machine-readable with proper Schema.org markup, creating a "fat card" of information LLMs can easily parse and reference.

Content Formats That Work

Q&A Pairs

LLMs handle FAQs exceptionally well—they mimic conversational training data. Structure questions and answers clearly with proper FAQ schema markup.

Example:

Q: What is Model Context Marketing?
A: Model Context Marketing is a marketing best practice for properly structuring, generating, and distributing content so that large language models can better understand it and recognize you as a domain expert worthy of citation.

Long-Form Articles

Context matters. Aim for 1,000-2,000+ word articles on focused topics. Depth signals expertise and provides LLMs with comprehensive information to draw from.

Example topics:

• "Model Context Marketing: How Brands Train AI to Understand Them"
• "Why Structured Data Will Define the Future of AI Search"
• "From SEO to AIO: Adapting Marketing for AI-First Discovery"

Voice Transcripts

Voice memos and transcripts offer authentic, conversational expertise. Use AI to clean up transcripts while preserving the natural explanatory flow and domain knowledge. This approach scales content creation while maintaining genuine authority.

Implementation

This site follows these principles through its memo system, structured definitions, comprehensive FAQs, and knowledge graph architecture. Each piece of content is designed to be both human-readable and machine-parseable, with multiple layers of structured data helping LLMs understand context and relationships.

Why This Matters

LLMs don't respond to traditional marketing tactics. They're trained on factual, well-structured information. By creating content that matches how LLMs learn and understand information, you position your brand as an authoritative source they can confidently cite and recommend.