EmailContact
Skip to main content

Core Concepts in LLM-SEO

🎯 Quick Summary​

  • Master the foundational concepts that define LLM optimization and AI search visibility
  • Understand how Citation Rate, AI Share of Voice, and other key metrics work
  • Learn the technical foundations: RAG systems, training data, and content governance
  • Build mental models for thinking about AI-powered search vs traditional search

📋 Table of Contents​

  1. LLM Optimization (LLMO)
  2. Citation Rate
  3. AI Share of Voice
  4. RAG Systems
  5. Foundation Model Training
  6. E-E-A-T for AI
  7. Content Governance
  8. Answer Engine Optimization
  9. Zero-Click Reality

🔑 Key Concepts at a Glance​

  • LLMO (LLM Optimization): The practice of optimizing content for AI model citation
  • Citation Rate: % of relevant queries where AI cites your content
  • ASoV (AI Share of Voice): Your visibility share in AI answers vs competitors
  • RAG (Retrieval-Augmented Generation): Real-time web search + AI generation
  • Training Data: Content used to train foundation models
  • E-E-A-T: Expertise, Experience, Authoritativeness, Trustworthiness signals
  • AEO (Answer Engine Optimization): Structuring content to directly answer questions

🏷️ Metadata​

Tags: core-concepts, llm-seo, fundamentals, education Status: %%ACTIVE%% Complexity: %%MODERATE%% Max Lines: 450 (this file: 445 lines) Reading Time: 10 minutes Last Updated: 2025-01-18

LLM Optimization (LLMO)​

What is LLMO?​

LLM Optimization (LLMO) is the practice of structuring and optimizing content to maximize citation and visibility in AI-powered search systems like ChatGPT, Claude, Gemini, and Perplexity.

Why It Matters​

Traditional SEO optimizes for:

  • Search engine crawlers (Googlebot)
  • Ranking algorithms (PageRank, etc.)
  • SERP (Search Engine Results Page) position
  • Click-through to your website

LLMO optimizes for:

  • AI model understanding and retention
  • Citation as an authoritative source
  • Presence in AI-generated answers
  • Brand recognition without clicks

Core Principles​

1. Semantic Clarity

Traditional SEO: "CRM software solutions for businesses"
LLMO: "What is CRM software? Customer Relationship Management
       (CRM) software helps businesses track customer interactions..."

2. Structured Data

<!-- Traditional SEO -->
<h1>Top 10 CRM Tools</h1>
<p>Here are the best CRM tools...</p>

<!-- LLMO -->
<h1>What are the best CRM tools?</h1>
<div itemscope itemtype="https://schema.org/FAQPage">
  <div itemscope itemprop="mainEntity" itemtype="https://schema.org/Question">
    <h2 itemprop="name">What is the #1 CRM for small businesses?</h2>
    <div itemscope itemprop="acceptedAnswer" itemtype="https://schema.org/Answer">
      <p itemprop="text">HubSpot CRM is the top choice...</p>
    </div>
  </div>
</div>

3. Authority Signals

  • Author credentials and bylines
  • Citations to authoritative sources
  • Expert quotes and contributions
  • Publication date and update frequency

Citation Rate​

Definition​

Citation Rate = (Number of citations) / (Number of relevant queries) × 100%

Example Calculation​

Test Queries: 100 (related to "project management software")
Times Cited: 23

Citation Rate = 23/100 × 100% = 23%

Benchmarks​

Citation RateRatingMeaning
0-5%🔴 PoorContent barely cited
5-15%🟡 Below AvgSome visibility
15-25%🟢 AverageIndustry standard
25-40%✅ GoodStrong performance
40%+🌟 ExcellentMarket leader

Factors That Influence Citation Rate​

Content Quality

  • Depth and comprehensiveness
  • Accuracy and up-to-date information
  • Clear, well-structured writing

Technical Optimization

  • Schema markup implementation
  • Semantic HTML structure
  • Clean, crawlable architecture

Authority Signals

  • Domain authority and age
  • Backlinks from trusted sources
  • Expert author credentials

Recency

  • Publication and update dates
  • Time-sensitive accuracy
  • Freshness signals

AI Share of Voice​

Definition​

AI Share of Voice (ASoV) measures your brand's relative visibility in AI-generated answers compared to competitors.

Calculation​

ASoV = (Your Citations) / (Total Category Citations) × 100%

Example:
Total queries about CRM: 500
Your brand mentioned: 60 times
Competitor A: 140 times
Competitor B: 100 times
Others: 200 times

Your ASoV = 60/500 × 100% = 12%
Competitor A ASoV = 140/500 × 100% = 28%
Competitor B ASoV = 100/500 × 100% = 20%

Why ASoV Matters​

Market Positioning

  • Shows who dominates AI-generated answers
  • Identifies gaps and opportunities
  • Tracks competitive shifts

Brand Awareness

  • Users form opinions based on AI citations
  • First/primary citations matter most
  • Repeated mentions build authority

Strategic Planning

  • Allocate resources to high-impact topics
  • Identify where competitors are weak
  • Track ROI from LLMO efforts

RAG Systems (Retrieval-Augmented Generation)​

What is RAG?​

RAG combines:

  1. Retrieval: Real-time web search
  2. Augmentation: Adding found info to prompt
  3. Generation: AI creates answer using both training + retrieved data

How RAG Works​

User Query: "What are the best CRM tools in 2025?"
     ↓
[1. RETRIEVAL]
System searches web for recent, relevant content
Finds: 10-20 relevant pages
     ↓
[2. AUGMENTATION]
Extracts key information from found pages
Adds to context window with original query
     ↓
[3. GENERATION]
LLM generates answer using:
- Its training data (what it already knows)
- Retrieved content (fresh information)
- Instructions to cite sources
     ↓
Output: "The best CRM tools in 2025 include..."
        [Citations: source1.com, source2.com, source3.com]

Why RAG Matters for LLMO​

Opportunity: Real-Time Visibility

  • Your content can be cited even if not in training data
  • Fresher content often preferred
  • Directly measurable impact

Platforms Using RAG

  • ✅ Perplexity AI (heavily RAG-based)
  • ✅ ChatGPT with web browsing
  • ✅ Google Gemini
  • ✅ Microsoft Copilot
  • ⚠️ Claude (limited web access)

Optimization Strategy

For RAG Citation:
├─ Semantic clarity (easy to extract answers)
├─ Structured data (machines can parse)
├─ Clear attribution (author, date, source)
└─ Crawlability (allow AI bots, fast loading)

Foundation Model Training​

What is Training Data?​

Foundation models (GPT-4, Claude, Gemini) are trained on massive text datasets scraped from the web.

Training Process​

1. Data Collection

Common Crawl (public web archive)
    ↓
Filters applied (remove spam, adult content, etc.)
    ↓
~trillions of words
    ↓
Training dataset

2. Training Model learns patterns, facts, writing styles from this data

3. Knowledge Cutoff Training data has a cutoff date (e.g., "April 2023")

Impact on Citations​

If Your Content is in Training Data:

  • ✅ Model has "memorized" facts from your site
  • ✅ May cite you from memory
  • ✅ Stronger authority association

If Your Content is NOT in Training Data:

  • ⚠️ Must rely on RAG for citations
  • ⚠️ Less brand recognition
  • ⚠️ Competitors with older content may have advantage

How to Get in Training Data​

For Future Training Cycles:

  1. Publish consistently - regular content signals active site
  2. Build authority - backlinks, mentions, trust signals
  3. Avoid blocks - don't block AI crawlers unnecessarily
  4. Create value - high-quality, unique content preferred

Timeline:

  • Training cycles: Every 6-18 months (varies by model)
  • Next opportunities: Likely 2025-2026 for major models
  • Impact: Citations may improve after training refresh

E-E-A-T for AI Systems​

What is E-E-A-T?​

E-E-A-T = Expertise, Experience, Authoritativeness, Trustworthiness

Originally a Google concept, now critical for AI citation.

The Four Pillars​

1. Expertise

Signals AI systems look for:
✅ Author credentials ("By Dr. Jane Smith, CRM Expert")
✅ Topic-specific expertise
✅ Technical depth and accuracy
✅ Industry certifications/affiliations

2. Experience

First-hand experience signals:
✅ "We tested 15 CRM tools over 6 months"
✅ Case studies and real examples
✅ Screenshots, data, specific details
✅ Personal insights and lessons learned

3. Authoritativeness

Authority indicators:
✅ Backlinks from trusted sites
✅ Media mentions and citations
✅ Speaking engagements, publications
✅ Social proof (followers, engagement)

4. Trustworthiness

Trust signals:
✅ HTTPS and security
✅ Clear author bios and contact info
✅ Transparent sourcing (cite your sources)
✅ Regular content updates
✅ Fact-checking and accuracy

Implementing E-E-A-T​

Author Bylines:

<article itemscope itemtype="https://schema.org/Article">
  <div itemprop="author" itemscope itemtype="https://schema.org/Person">
    <span itemprop="name">Dr. Jane Smith</span>
    <span itemprop="jobTitle">CRM Industry Analyst</span>
    <span itemprop="affiliation">SoftwareReview Institute</span>
  </div>
</article>

Citation of Sources:

❌ "Studies show CRM improves sales."
✅ "A 2024 Harvard Business Review study found that
    CRM implementation improves sales by 29% on average."

Content Governance​

What is Content Governance?​

Content Governance = Controlling how AI systems access and use your content.

Tools for Governance​

1. robots.txt

# Allow all AI crawlers
User-agent: *
Allow: /

# Block specific AI crawlers
User-agent: GPTBot
Disallow: /

User-agent: Google-Extended
Disallow: /private/

User-agent: anthropic-ai
Allow: /public/
Disallow: /private/

2. Meta Tags

<!-- Block AI training, allow RAG indexing -->
<meta name="robots" content="noai-train, index, follow">

<!-- Allow everything -->
<meta name="robots" content="all">

3. Legal Signals

<!-- Terms of Service link -->
<link rel="terms-of-service" href="/terms">

<!-- Explicit AI usage policy -->
<meta name="ai-usage-policy" content="https://example.com/ai-policy">

Strategic Considerations​

Allow AI Access:

  • ✅ Public-facing content you want cited
  • ✅ Educational content building authority
  • ✅ Product information for discovery

Restrict AI Access:

  • ❌ Private/sensitive information
  • ❌ Paywalled premium content
  • ❌ User-generated content (legal liability)

Answer Engine Optimization (AEO)​

What is AEO?​

AEO = Structuring content to be the direct answer to user questions.

AEO vs SEO​

Traditional SEOAEO (Answer Engine)
Optimize for page rankingsOptimize for being quoted
Drive clicks to your siteProvide direct answers
Keyword densitySemantic clarity
Backlinks for authorityE-E-A-T signals

AEO Techniques​

1. Question-Answer Format

## What is the best CRM for small businesses?

HubSpot CRM is the top choice for small businesses because:
1. Free forever plan (unlimited users)
2. Simple setup (under 10 minutes)
3. Integrates with 500+ tools

2. Definition Lists

<dl>
  <dt>CRM (Customer Relationship Management)</dt>
  <dd>Software that helps businesses manage customer
      interactions, track leads, and automate sales processes.</dd>
</dl>

3. FAQ Schema

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "How much does CRM software cost?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "CRM software ranges from free (HubSpot, Zoho)
               to $300+/user/month (Salesforce Enterprise)."
    }
  }]
}
</script>

Zero-Click Reality​

Zero-Click = User gets answer from AI without visiting any website.

The Shift​

Traditional Search Journey:

User → Google → SERP → Click → Your Website → Answer

AI-Powered Search Journey:

User → ChatGPT → Answer (maybe citation)

Impact:

  • ❌ No direct traffic
  • ❌ No ad impressions
  • ❌ No conversion opportunities
  • ✅ Brand awareness (if cited)
  • ✅ Authority building
  • ✅ Trust development

Adapting to Zero-Click​

Mindset Shift:

  • Optimize for brand mentions, not just traffic
  • Track citations as primary metric
  • Build authority that compounds over time

Business Model Implications:

  • Consider citation as top-of-funnel
  • Retargeting via brand search
  • Direct traffic from brand awareness

📚 Related Topics​

Deep Dives:

Practical Guides:

Metrics:

🆘 Need Help?​

Still confused?

Ready to optimize?

Last updated: 2025-01-18 | Edit this page | Report issue

Last updated on