Common questions about Model Context Marketing and creating LLM-friendly content
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. It focuses on creating factual, well-structured content that AI can parse, verify, and confidently recommend.
Search engines aren't dying—they're losing their position as the dominant force for information discovery. We call this "Search Engine Decay." Users are slowly shifting to AI-powered conversational interfaces that provide instant answers without the multi-click journey. ChatGPT reached 1 billion users in 36 months. Within 10 years, search engines will likely transition from discovery tools to verification tools, while AI becomes the primary channel for finding information.
Website Fatigue is the exhaustion users experience with bloated, complex websites. Users don't want to scroll through 500-900 pages or navigate complex menus just to answer three basic questions: What do you have? How much does it cost? Can I use it? We live in an instant gratification era. Websites should be tight—10-20 core pages with transparent pricing and clear navigation—not elaborate journeys that hide information behind gates.
LLMs are trained on factual, research-based information and filter out promotional language. Traditional marketing tactics like hype, spin, and sales-focused content do not resonate with large language models. They prioritize authoritative sources that demonstrate genuine domain expertise through verifiable facts and clear explanations.
Use semantic HTML tags for proper content hierarchy, implement JSON-LD structured data with Schema.org vocabulary, ensure server-side rendering so content is visible to crawlers, create factual and research-based content with clear examples, provide proper metadata and canonical URLs, and explicitly allow LLM crawlers in your robots.txt file.
JSON-LD (JavaScript Object Notation for Linked Data) is the recommended format for adding structured data to web pages. It uses Schema.org vocabulary to provide explicit, machine-readable context about your content. This helps LLMs understand what entities exist on your page, how they relate to each other, and what facts can be extracted and cited.
Semantic HTML tags provide meaning about content structure beyond just presentation. Tags like <header>, <main>, <article>, <section>, and <nav> tell both browsers and AI systems what role each piece of content plays. Proper use of semantic tags and heading hierarchy (H1 → H2 → H3) helps LLMs understand relationships between topics and subtopics.
Aim for 1,000-2,000+ words for substantive topic articles. Context matters—longer, comprehensive content signals expertise and provides LLMs with enough information to understand your domain authority. However, length should serve clarity, not just word count. Every section should add value.
Yes. Voice memos and transcripts offer authentic, conversational expertise that can be cleaned up with AI while preserving natural explanatory flow and domain knowledge. This approach scales content creation while maintaining genuine authority, as the content comes from real expertise rather than generic AI-generated text.
A knowledge graph is a network of interconnected concept pages with proper structured data markup. Each page defines an entity (concept, product, person) with machine-readable Schema.org markup and links to related entities. This creates a "fat card" of information that LLMs can easily parse, understand relationships between concepts, and cite authoritatively.
Traditional SEO focuses on ranking high in keyword-based search results through tactics like keyword optimization and backlinks. Model Context Marketing focuses on being cited by AI models through factual, structured content that demonstrates domain expertise. SEO targets search engines; MCM targets the LLMs that power AI-generated answers. They're complementary strategies—you should do both. As AI adoption grows alongside search, MCM becomes increasingly critical.
Don't choose between them—do both. Continue traditional SEO (search engines still drive significant traffic) AND invest in AI optimization. The smart approach is to diversify your strategy. AI is growing exponentially as a channel, but search engines aren't disappearing overnight. The winners will be marketers who optimize for both traditional search and AI-powered discovery. Those who adapt early to AI will have competitive advantages as the shift accelerates.
Allow ChatGPT-User, GPTBot, Claude-Web, CCBot, anthropic-ai, PerplexityBot, and Google-Extended at minimum. These represent major LLMs that crawl the web to update their knowledge. Explicitly allowing these crawlers ensures your content can be discovered and understood by AI systems.
Factual content includes specific numbers, concrete examples, verifiable claims, technical details, and clear explanations. Avoid vague superlatives ("best," "leading," "innovative"), unsubstantiated claims, and promotional language. Focus on what, how, and why with evidence and examples rather than marketing spin.
Not entirely, but promotional sections should be clearly separated from educational content. LLMs can distinguish between informational content and sales pages. Keep educational resources factual and authoritative, while product/sales pages can be more promotional. Structure your site so LLMs can easily identify and cite the educational portions.