How AI Models Actually Work: Understanding the Foundations of GEO

In our previous article, we introduced Generative Engine Optimization (GEO) as the new frontier for brand visibility in the age of AI assistants. To develop an effective GEO strategy, however, you need to understand how these AI systems actually work—and dispel some common misconceptions along the way.

The Brain Behind the Bot: How AI Language Models Function

When you ask ChatGPT a question about your favorite running shoes or request Claude to summarize your company's history, you're interacting with a Large Language Model (LLM). But what exactly is happening behind the scenes?

Training on a Corpus: The Digital Education

AI language models like ChatGPT, Claude, and Gemini begin their existence by "reading" vast amounts of text data—their training corpus. This corpus typically includes:

• Billions of web pages
• Books and academic papers
• Social media posts
• Code repositories
• Wikipedia articles
• And much more

During training, these models process this information using a neural network architecture called a "transformer," which allows them to understand patterns, relationships, and contexts within language.

Technical Note: Modern LLMs use word embeddings—multi-dimensional vector representations of words—that place semantically similar words close together in vector space. This allows the model to understand relationships between concepts even when they're expressed using different terminology.

The scale of this training is staggering. For example:

• GPT-4 was trained on hundreds of billions of words
• These models contain hundreds of billions of parameters (the adjustable values that determine how the model processes information)
• Training can require thousands of high-performance GPUs running for months

Why AI Models Aren't Just Fancy Search Engines

One of the most common misconceptions about AI language models is that they function like search engines—simply retrieving and regurgitating information from their training data. This fundamental misunderstanding leads to ineffective GEO strategies.

Here's why AI models are fundamentally different from search engines:

Search Engines	AI Language Models
Index and retrieve existing content	Generate new content based on patterns learned
Return links to source material	Create responses without direct references
Update continuously with new web content	Fixed knowledge as of training cutoff date
Rank results based on relevance algorithms	Generate responses based on statistical patterns
Can cite sources directly	Cannot reliably cite sources

The Magic of Emergence: Knowledge Beyond Training

Perhaps the most fascinating aspect of large language models is their ability to "know" things that weren't explicitly in their training data. This phenomenon, known as emergent knowledge, occurs when models synthesize information across their entire training corpus to form new connections.

For example, an AI model might never have seen the explicit statement "Company X was founded before Company Y," but if it learned separately that Company X was founded in 1985 and Company Y in 1990, it can deduce the chronological relationship when asked.

This emergent property means that AI models can sometimes make accurate inferences about your brand even if those specific facts weren't directly stated in their training data—for better or worse.

The Personality Differences Between AI Models

Not all AI models are created equal. Each has its own "personality" and tendencies based on its:

• Training data: Different models are trained on different datasets

• Architecture: The specific design of the neural network

• Parameter count: Generally, more parameters allow for more nuanced understanding

• Fine-tuning: Additional training for specific capabilities or safety guardrails

• ChatGPT (OpenAI)

  • Tends to be conversational and helpful
  • Often provides detailed explanations
  • Generally cautious about making definitive claims about brands
  • May refuse to answer certain types of questions due to safety constraints

• Claude (Anthropic)

  • Often more nuanced in its responses
  • Designed with a focus on harmlessness and helpfulness
  • Sometimes more willing to engage with hypotheticals
  • May provide more context around limitations of its knowledge

• Gemini (Google)

  • Integrates more recent information through search capabilities
  • Often includes citations or references
  • Designed to be more factual and less conversational
  • May have different tendencies when discussing commercial entities

These personality differences mean that your brand might be perceived differently across various AI models—a critical consideration for comprehensive GEO.

The Black Box Problem: Why Measuring GEO is Challenging

Unlike SEO, where you can directly observe your ranking position for specific keywords, GEO presents unique measurement challenges:

1. Non-deterministic Responses

   AI models don't always give the same answer to the same question. Ask ChatGPT about the best running shoes five times, and you might get five slightly different responses, with different brands mentioned in different orders.

2. Context Sensitivity

   The way a question is phrased dramatically affects the response. "What are good running shoes?" might yield different results than "What running shoes should I buy for a marathon?"

3. Lack of Transparency

   We don't have access to the complete training data or the exact weights and parameters that determine how these models respond to queries about your brand.

4. Continuous Evolution

   AI models are regularly updated, and their responses can change over time, even without new training data, as their systems are refined.

The Illusion of Control: Why Traditional SEO Tactics Fall Short

Many marketers approach GEO with an SEO mindset, assuming they can directly influence how AI models perceive their brand through traditional optimization techniques. This approach misunderstands the fundamental nature of these systems.

Unlike search engines, where algorithm updates are frequent and website changes can quickly impact rankings, AI language models:

• Are trained on historical data with specific cutoff dates
• Cannot be directly influenced by new content until they're retrained
• Don't have a direct mechanism to prioritize "authoritative" sources in the way search engines do
• Don't follow links or crawl websites in real-time

This doesn't mean GEO is impossible—it just requires a different approach and timeline than traditional SEO.

Practical Implications for Your GEO Strategy

Understanding how AI models work leads to several important strategic insights:

1. Historical Presence Matters

   The information available about your brand before the model's training cutoff date has an outsized influence on how it perceives you. Established brands with extensive online presence have an inherent advantage.

2. Volume and Consistency Are Key

   Information that appears consistently across many sources in the training data is more likely to be "learned" by the model. Contradictory information creates uncertainty in the model's responses.

3. Context and Associations Shape Perception

   AI models understand concepts through their relationships to other concepts. Your brand's associations (industry, competitors, key people, events) shape how models contextualize and present information about you.

4. Prepare for the Next Training Cycle

   While you can't change how current models perceive your brand, you can create content now that will influence future versions when they're trained on newer data.

Looking Ahead: The Future of AI Model Training

As AI technology evolves, we're likely to see changes in how these models are trained and updated:

• Continuous learning: Models may be updated more frequently with new data
• Specialized models: Industry or domain-specific AI assistants with deeper knowledge in particular areas
• Feedback mechanisms: Systems that allow brands to correct misinformation
• Citation capabilities: Models that can more reliably cite sources for their claims

These developments will create new opportunities and challenges for GEO practitioners.

Conclusion: Knowledge is Power in GEO

Understanding how AI language models actually work—beyond the simplified explanations often found in marketing materials—is the foundation of effective generative engine optimization. By recognizing that these systems aren't search engines but complex statistical models with emergent properties, you can develop more realistic expectations and more effective strategies for influencing how your brand is perceived in the AI era.

In our next article, Measuring Your Brand's GEO Performance, we'll explore practical methods for measuring your brand's current GEO performance across different AI models, providing you with a baseline for improvement.

Curious about how AI models perceive your brand? Visit AIRanksEverything.com to get started with a free brand analysis and take the first step toward effective generative engine optimization.

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This article is part of our comprehensive series on Generative Engine Optimization:

• What is GEO (Generative Engine Optimization)?
• How AI Models Actually Work (You are here)
• Measuring Your Brand's GEO Performance
• GEO Strategy: Improving Your Brand's Standing