LLM Fundamentals for Marketers: Tokens, Vectors, and How AI "Thinks"

Why Marketers Need to Understand LLMs

You don't need to build an AI model. But you DO need to understand how AI processes your content so you can optimize for it.

Think of it like SEO: you don't need to build a search engine, but understanding how crawling, indexing, and ranking work helps you optimize your site.

What is an LLM?

A Large Language Model (LLM) is an AI system trained on massive text data to understand and generate human language. Examples: GPT-4, Gemini, Claude, LLaMA.

LLMs power all the AI search engines that matter for GEO:

• ChatGPT → GPT-4 / GPT-4o
• Google Gemini → Gemini 2.0
• Perplexity → Multiple models
• Claude → Claude 3.5
• Copilot → GPT-4 via Microsoft

How LLMs Process Your Content

Step 1: Tokenization

LLMs break text into tokens — chunks of text that can be words, parts of words, or characters.

• "Generative Engine Optimization" → ["Gener", "ative", " Engine", " Optim", "ization"]
• Average: 1 token ≈ 0.75 English words
• Why it matters: AI has a context window (token limit). Content that's concise and information-dense is processed more effectively.

Step 2: Vector Embedding

Each token is converted into a vector — a list of numbers representing its meaning in a multi-dimensional space.

• Words with similar meanings have similar vectors
• "GEO" and "SEO" are close in vector space
• "GEO" and "banana" are far apart
• This is how AI understands meaning and relationships

Step 3: Attention Mechanism

The AI determines which parts of your content are most relevant to the user's query:

• Clear headings help AI focus attention
• Direct answers get more attention weight
• Well-structured content is easier to process
• Noise and fluff reduce the signal

Step 4: Generation

Based on the processed context, the AI generates a response, citing sources it deems most relevant and authoritative.

Key Concepts for GEO

Context Window

The maximum amount of text an LLM can process at once:

• GPT-4: 128K tokens (~96,000 words)
• Claude 3.5: 200K tokens
• Gemini 2.0: 2M tokens

GEO implication: Your content competes with thousands of other pages in the context window. Clarity and density win.

Semantic Distance

How "close" two concepts are in the AI's understanding:

• Close: "GEO" ↔ "AI search optimization" (very close)
• Medium: "GEO" ↔ "digital marketing" (somewhat close)
• Far: "GEO" ↔ "cooking recipes" (very far)

GEO implication: Build content that reduces the semantic distance between your brand and your key topics.

Retrieval-Augmented Generation (RAG)

Modern AI search uses RAG:

User asks a question

AI searches the web for relevant content

Retrieved content enters the context window

AI generates an answer citing the best sources

GEO implication: Your content needs to be BOTH findable (good SEO) AND extractable (good GEO) to be cited.

Information Gain vs Knowledge Gain

• Information Gain: Content that adds NEW information beyond what AI already knows
• Knowledge Gain: Content that helps AI connect existing knowledge in new ways

GEO implication: Original research, unique data, and novel frameworks have the highest GEO value. AI doesn't need another generic "What is SEO?" article.

Reinforcement Learning from Human Feedback (RLHF)

AI models are fine-tuned based on human preferences:

• Humans rate AI responses for quality
• AI learns to prefer certain source types
• This is why authoritative, well-structured content gets cited more

GEO implication: AI is trained to prefer what HUMANS prefer — high-quality, authoritative, well-structured content.

Practical Takeaways for Marketers

Write clear, dense content — every sentence should carry meaning

Use explicit definitions — AI processes these more reliably

Structure with clear headers — helps AI's attention mechanism

Include original data — information gain is what differentiates you

Be the authority — AI cites what RLHF has taught it is trustworthy

Keep content fresh — RAG-based engines prefer recent content

Think in "chunks" — each paragraph should be independently extractable

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Next steps: What is GEO?

E-E-A-T-A Framework

Schema Markup for GEO