LLMO Strategies for Content Creators: Optimizing for AI Discovery

Chris McNeilly
Oct 8
4 min read

This is the second post in our three-part series on the evolution from SEO to LLMO. If you haven't read Part 1: The Rise of LLMO, we recommend starting there to understand the fundamental shift in how people are discovering information online. After finishing this guide, check out Part 3: The Future of Search and the New Citation Economy for insights on what's coming next.

Beyond Keywords: A New Approach to Content Optimization

In our first post, we explored how the rise of large language models (LLMs) is changing the way people find information online. Now, let's dive into the practical strategies content creators can use to ensure their work thrives in this new AI-driven discovery landscape.

The goal of LLMO isn't just to rank well in search results, but to become a trusted, frequently cited source that LLMs reference when answering user queries. This requires a different approach to content creation and optimization.

The Foundation: E-E-A-T for LLMO

Google's E-E-A-T principles (Experience, Expertise, Authoritativeness, and Trustworthiness) have become even more critical in the age of LLMs. AI systems are designed to prioritize reliable information from credible sources, making these qualities essential for LLMO:

Experience: Demonstrate first-hand experience with your subject matter. Personal stories, case studies, and original research all signal to LLMs that your content offers unique value.
Expertise: Clearly establish your credentials and depth of knowledge. Author bios, professional affiliations, and consistent topic focus all help LLMs identify your content as expert-created.
Authoritativeness: Build recognition within your field. Citations from other respected sources, industry awards, and media mentions all contribute to authoritativeness signals that LLMs can detect.
Trustworthiness: Provide accurate, well-sourced information. Cite reliable sources, keep content updated, and maintain transparent practices around sponsorships or conflicts of interest.

Content Structure for LLMO

How you organize information dramatically affects how well LLMs can understand and reference your content:

Clear, Factual Headings

LLMs often use headings to understand content structure and identify relevant sections. Optimize your headings by:

Using descriptive, fact-based language
Structuring content hierarchically (H1, H2, H3)
Including key terms and concepts in headings
Avoiding clickbait or overly creative headings that obscure the topic

Structured Data Points

When presenting factual information, use formats that are easy for both humans and AI to parse:

Definition lists for terminology
Comparison tables for product features
Chronological timelines for historical information
Clearly labeled statistics with citation information

Rich Context and Relationships

LLMs excel at understanding relationships between concepts. Help them by:

Explicitly stating connections between related topics
Using clear transition language between sections
Providing context for specialized terminology
Defining acronyms and industry jargon

Creating Citation-Worthy Content

The currency of LLMO is citation—your goal is to create content that LLMs will reference when answering relevant queries:

Definitive Statements

LLMs are more likely to cite content that makes clear, authoritative statements rather than vague or hedged language:

Less Effective: "Some people might find that using retinol could potentially help with certain skin issues in some cases."

More Effective: "Retinol increases cell turnover by 40% and stimulates collagen production, making it effective for reducing fine lines and improving skin texture, according to a 2023 study in the Journal of Dermatology."

Unique Data and Research

Original research, surveys, and data analysis are gold for LLMO. LLMs prioritize referencing primary sources of information:

Conduct industry surveys and publish the findings
Analyze public data in new ways that provide unique insights
Document case studies with specific metrics and outcomes
Create original frameworks and methodologies

Comprehensive Coverage

LLMs favor sources that provide complete information on a topic:

Cover all aspects of a subject, including less common scenarios
Address obvious questions and objections
Include relevant limitations and exceptions
Provide context about when information applies and when it doesn't

The Technical Side of LLMO

Beyond content quality, several technical considerations can improve your LLMO performance:

Structured Data Markup

Implementing schema.org markup helps LLMs better understand your content:

Use Article schema for blog posts
Implement FAQPage schema for Q&A content
Apply Product schema for product information
Include Author and Organization schema to establish authority

Natural Language Accessibility

Make your content easy for AI systems to process:

Ensure clean HTML with semantic markup (proper use of <article>, <section>, etc.)
Provide text alternatives for non-text content
Use descriptive alt text for images
Implement proper heading hierarchy

Citation-Friendly Formatting

Make it easy for LLMs to cite your work accurately:

Include clear publication dates
Provide author information
Use consistent formatting for statistics and claims
Structure quotes and references for easy extraction

Measuring LLMO Success

Unlike SEO, where ranking positions and traffic provide clear feedback, measuring LLMO success requires different approaches:

Monitor mentions of your brand or content in AI responses
Track citation frequency using specialized LLMO tools (discussed more in Part 3)
Analyze traffic patterns from AI assistant platforms
Test queries related to your content across multiple LLMs

Balancing SEO and LLMO

The most effective content strategies now need to address both traditional search and LLM discovery:

Maintain keyword relevance while focusing on comprehensive, authoritative content
Balance scannable content for humans with structured data for AI understanding
Create content that works for both direct consumption and as an AI reference source
Develop separate content strategies for highly competitive SEO terms versus LLMO opportunities

Case Study: The LLMO Advantage

The health and wellness brand Nourish saw a 32% increase in brand mentions across major AI assistants after implementing a comprehensive LLMO strategy. Their approach included:

Creating definitive guides on specific nutritional topics
Publishing original research on supplement efficacy
Implementing comprehensive schema markup
Establishing clear authorship from credentialed experts
Structuring content with AI-friendly formatting

While their traditional search traffic remained stable, they gained significant exposure through AI assistants, ultimately driving a 24% increase in direct brand searches.

Looking Ahead

As we'll explore in Part 3 of this series, the metrics and economics of digital discovery are evolving rapidly. The most successful content creators will be those who adapt their strategies to succeed in both traditional search and the emerging LLMO landscape.

By focusing on creating genuinely valuable, uniquely insightful, and technically optimized content, you'll build a foundation for sustainable visibility in the age of AI-mediated discovery.

lerating your AI adoption from top of funnel on down? Contact me at chris@clarityailabs.com or visit www.clarityailabs.com for expert guidance on your AI journey.