A Simple View of How LLMs Work

Okay, we've established that a Large Language Model, or LLM, is a type of AI designed to understand and generate human-like text. But how does it actually work? Let's look at a simplified view, without getting lost in the complex mathematics just yet.

Think of an LLM as an extremely advanced pattern-matching machine combined with a sophisticated prediction engine. It has been trained on an enormous amount of text data – books, articles, websites, code, and more. During this training phase, the model didn't "learn" facts in the way a human does by understanding meaning. Instead, it learned statistical relationships between words and sequences of words. It figured out, based on countless examples, which words are likely to follow other words in different contexts.

For example, after seeing the phrase "The quick brown fox jumps over the lazy..." millions of times in its training data, the model learns that the word "dog" is extremely likely to come next. It learns grammatical rules, common phrases, associations between concepts (like "sky" and "blue"), and even writing styles, all as patterns derived from the data.

So, when you give an LLM a prompt (the input text), it doesn't understand your request in a human sense. Instead, it does the following:

1. Analyzes the Input: It looks at the sequence of words you provided.
2. Consults Learned Patterns: It uses the massive network of statistical patterns it learned during training to determine what words are most likely to follow your input sequence, based on everything it has processed before.
3. Predicts the Next Word: It calculates probabilities for potential next words and typically chooses the most probable one (or one of the highly probable ones, allowing for some creativity).
4. Appends and Repeats: It adds this predicted word to the sequence and then repeats the process: it looks at the new, longer sequence and predicts the next most likely word, and so on.

It builds its response one piece at a time, constantly predicting what should come next based on the prompt and the text it has generated so far.

A simplified flow showing how an LLM processes a prompt to generate text.

This predictive, step-by-step process is why LLMs are often described as "next-word predictors" on a grand scale. Their ability to generate coherent, contextually relevant, and often surprisingly creative text comes from the sheer scale of their training data and the complexity of the patterns they've learned, not from genuine comprehension or consciousness. In the next section, we'll look more closely at the "pieces" these models work with, called tokens.