How Large Language Models (LLMs) Work: Text, Tokens, and Predictions

Large language models (LLMs) like ChatGPT, Claude, and Gemini have transformed how we interact with AI. But how do they actually work? Understanding the mechanics helps marketers use these tools more effectively.

From Text to Tokens

LLMs don't read text the way humans do. Instead, they break text into tokens — chunks of characters that represent meaningful units. The word "marketing" might be one token, while "uncharacteristically" might be broken into several.

This tokenization is why LLMs sometimes struggle with very long documents or precise counting tasks — they're working with tokens, not characters or words.

Prediction at Scale

At their core, LLMs predict what comes next. Given the text "The best digital marketing strategy is...", the model calculates probabilities for every possible next token and selects one (with some randomness for creativity).

This is trained on enormous datasets — essentially a compressed representation of human writing.

What This Means for Marketers

• Specificity wins: More context = better outputs
• Iteration is necessary: First drafts are starting points
• Hallucinations are real: Always verify facts the model generates