The Marketer's Guide to Large Language Models and AI Search

What is an LLM?

Large Language Models (LLMs) like ChatGPT, Claude, Gemini, and others are fundamentally reshaping how people find, consume, and act on information. Think of LLMs as incredibly well-read assistants who've consumed millions of books, articles, and websites that can give you answers, advice, and recommendations.

Unlike traditional search engines, which index static pages based on keyword metadata, LLMs are probabilistic systems designed to predict and generate language based on patterns learned from massive text information. They interpret context, generate sentences, and respond dynamically to queries, also known as “prompts."

Why are LLMs so smart?

Pre-Training

The first phase in the lifecycle of an LLM is pre-training. This is where the LLM is trained to predict the next word in a sentence based on massive text sources, including publicly available internet content, literature, and sometimes less transparent sources like Reddit or even public social media posts. Think of pre-training as where the LLM goes to college, forming deep knowledge and internal biases. The impact of these internal biases will come up later.

Post-Training

Once pre-training establishes the core language capabilities, the LLM goes through post-training, where it is fine-tuned to learn what a “good” answer is and how to generate responses that are more relevant, accurate, and helpful. Think of post-training as the post-graduate years when the LLM enters the workforce and mentors teach the LLM how to be helpful and professional.

Search Integration

No matter how much information an LLM model learns in its training phase, there will always be gaps in its knowledge. This is where search integration comes into play. If an AI model does not have the answer within its pre-existing knowledge, it will retrieve information from indexed websites in real time and incorporate that information into its response. 

How AI is Different From Search

You might think, wait…isn’t that just search? Kind of…but slightly different. Traditional search finds links. AI now reads, interprets, and answers using what it finds in real time using RAG (Retrieval-Augmented Generation). RAG helps AI go beyond what it was trained on by pulling in real-time, reliable information from external sources like websites or internal docs.

How RAG Works:

1. Searches for context: Before answering, the AI retrieves relevant info from trusted sources.
2. Uses that info to answer: It incorporates the retrieved content to generate more accurate responses.
3. Shows its sources: The AI includes links or citations so you can verify where the facts came from.

Remember how the LLM forms internal biases during its pre-training? These internal biases will impact how the AI model reads the internet. So as a marketer it's important to think about how you impact AI at both the training and search phases.

Let’s recap!

Why do responses vary so much? 

Responses will inherently vary within the same LLM and across different LLMs. This is by design. Because LLM responses are probabilistic (meaning they are subject to variability), each prompt generates a response sampled from a distribution of possible next-word sequences. This means if you give ChatGPT the same prompt 10 times, it might produce 10 different responses. 

Each LLM provider also has its own nuances of how it trains and fine-tunes the datasets that power the model. Variation stems from multiple factors:

• Data diversity: Different LLMs are trained on different information. Some lean heavily on Reddit, others on scientific journals, or even scraped social content.
• Training cycles: Each LLM provider has a different training cycle length  
• Fine-tuning: Each LLM provider adds behavioral layers that shape tone, safety, and formatting.

What does this mean for the future of search? 

Traditional SEO (Search Engine Optimization) focuses on optimizing for Google’s algorithm to rank higher and capture traffic through metadata, keyword density and backlink hierarchies. But LLMs do not “search” in the conventional sense. They generate responses from an internal semantic understanding and, increasingly, from integrated live data. As a result, the strategies that worked for SEO are no longer sufficient. 

LLMs understand meaning, not just words. They convert text into mathematical representations called vector embeddings—think of them as GPS coordinates where similar ideas like 'happy' and 'cheerful' live in the same neighborhood. Each word is mapped to a point in this space based on meaning, enabling the model to understand not just keywords but semantic similarity.

For example, if you ask “'What's the weather like?” and “How's it looking outside today?” —an LLM understands these mean the same thing, even though they use completely different words. This behavior explains why content creators should focus on topics and themes, not just specific keywords.

We’re moving away from keywords and towards context. This new era of AI Search focuses on influencing AI outputs through thematic and conceptual content rather than through keywords, backlinks, and metadata. 

What does this mean for me as a marketer?

Since LLMs learn from patterns across millions of texts, your brand's reputation isn't just about your website anymore—it's about every mention, review, and comparison across the entire internet. You can’t rely on a single prompt to understand how AI sees your brand. Or even focus on “winning” in a variety of specific prompts. AI’s perception is spread across thousands of micro-mentions and comparisons.

To get clarity you need scale. Brand perception in AI is relative—it lives in patterns, frequency and competitive context. Once you understand that picture, you can shape it to:

• Align messaging with how AI “thinks” about your category 
• Create content that educates AI in your favor
• Prioritize domains that influence model learning

Your GEO strategy as a marketer is more than just optimization. It’s about building your brand - with both your consumers and AI. 

Your new AI vocabulary

• GEO (Generative Engine Optimization): Strategy for influencing AI outputs through thematic and conceptual content.
• Large Language Model (LLM): A machine learning system trained to understand and generate text using massive datasets.
• Post-training: A behavioral fine-tuning phase that aligns the model with desired output formats and safety constraints.
• Pre-training: The initial phase where the model learns linguistic patterns and general knowledge.
• Reinforcement Learning from Human Feedback (RLHF): RLHF is part of the post-training stage of building an LLM. The pre-trained model is further trained by human evaluators who rate or rank the model's outputs, and the model learns to generate responses that humans prefer. Rather than learning from fixed examples, the model receives ongoing feedback about what makes outputs better or worse.
• Search Integration: The process by which an LLM performs live internet search if it does not inherently know the answer from its pre- and post-training.
• Semantic Similarity: The closeness in meaning between different phrases or prompts. For example, if you ask “'What's the weather like?” and “How's it looking outside today?” —an LLM understands these mean the same thing, even though they use completely different words.
• Supervised Fine-Tuning (SFT): SFT is part of the post-training stage of building an LLM. The pre-trained model is further trained on a curated dataset of input-output pairs that demonstrate the desired behavior. The model learns by trying to match the provided examples as closely as possible.‍
• Vector Embeddings: Mathematical representations of words or concepts in multi-dimensional space (think of it as GPS coordinates for ideas—”happy” and “cheerful” live in the same neighborhood).