LLM

A Large Language Model (LLM) is a deep learning model trained on massive text corpora to understand and generate human language. LLMs are the core technology behind every major AI chatbot and answer engine — ChatGPT, Claude, Gemini, Perplexity, and others are all built on top of LLMs.

How LLMs work at a high level

LLMs are trained through a process called next-token prediction: given a sequence of text, the model learns to predict what word (or token) comes next. Trained on hundreds of billions of words from the web, books, code, and other sources, the model builds an internal representation of language, facts, concepts, and relationships.

This internal representation is why LLMs can:

• Answer questions without being explicitly programmed with the answers
• Generate coherent, contextually appropriate text
• Reason across topics they’ve seen in training data

LLMs and brand representation

Here’s the critical insight for AEO/GEO: your brand lives inside LLMs as a learned representation. Whatever the model has absorbed about your company — from articles, reviews, press, forums, your own content — shapes every response that mentions you.

If the web says your brand is “reliable and innovative,” the model encodes that. If the web associates your brand with controversies, the model encodes that too. This is why content strategy, PR, and third-party coverage are directly relevant to AI visibility.

LLM limitations that affect visibility

• Knowledge cutoffs — LLMs have a training data cutoff date; events after that date are unknown unless the engine uses RAG
• Hallucination — models can generate plausible-sounding but incorrect information about your brand
• Inconsistency — the same question asked multiple times may yield slightly different answers, including different brand mentions

Key LLMs powering answer engines

Monitoring visibility across these engines means monitoring how your brand is represented across the different underlying models — each of which has its own training data and biases.