Lesson 15 — How Do Large Language Models Work?

We now know that an LLM is an AI model designed to work with language.

But how does it actually generate an answer?

The basic idea is surprisingly simple: an LLM learns patterns in language and uses those patterns to predict what should come next.

Language Prediction

At its core, a language model tries to predict the next piece of text.

For example, imagine the sentence:

“The sun rises in the…”

A language model can predict that a likely next word is “east”.

Modern LLMs repeat this process many times to generate longer responses.

LLMs Work With Tokens

LLMs do not process text exactly as humans see it.

Text is first broken into smaller pieces called tokens.

A token can be a complete word, part of a word, punctuation, or another small piece of text.

For example, a sentence might be split into several tokens before the model processes it.

From Tokens to Meaning

The model converts these tokens into numerical representations that it can process.

This allows the model to identify relationships between different parts of the text.

It can learn that certain words often appear together and that the meaning of a word can change depending on the surrounding text.

Why Context Matters

Context is extremely important for understanding language.

Consider the word “bank”.

In “I deposited money at the bank”, it refers to a financial institution.

In “We sat on the river bank”, it refers to the land beside a river.

The surrounding words help the model determine which meaning is more appropriate.

Attention Helps the Model Use Context

Modern LLMs use a mechanism called attention to determine which parts of the input are important when processing a piece of text.

This helps the model connect words and concepts that may be far apart in a sentence or conversation.

Attention is one of the key ideas behind the architecture used by modern LLMs.

Generating a Response

Once the model has processed your prompt, it predicts a likely next token.

It then uses the new text as part of the context and predicts another token.

This process continues until the model has generated a complete response.

In simplified form:

Read context → Predict next token → Add token → Repeat

Why Doesn’t the Model Always Give the Same Answer?

Language generation can involve choosing between several possible next tokens.

Depending on the model and its settings, different choices can sometimes produce different responses to the same prompt.

This is one reason AI-generated text can vary from one response to another.

What About Training?

Before an LLM can generate useful text, it must first be trained on large amounts of data.

During training, the model repeatedly practices predicting missing or next pieces of text and adjusts its internal parameters.

Over time, this allows the model to learn complex patterns in language.

Does an LLM Think Like a Human?

Not necessarily.

An LLM can produce answers that appear thoughtful or logical, but its basic operation is based on processing patterns and predicting tokens.

This distinction is important when understanding what modern AI can and cannot do.

A Simple Analogy

Imagine an extremely advanced autocomplete system that has learned from an enormous amount of text.

Instead of predicting only one word, it can repeatedly predict the next token to build entire paragraphs, explanations, stories, or pieces of code.

This is a simplified way to understand the basic principle behind language generation.

Key Takeaways

• LLMs generate text by predicting what comes next.
• Text is processed as smaller pieces called tokens.
• Context helps the model understand how words relate to each other.
• Attention helps the model focus on relevant parts of the context.
• The model generates responses one token at a time.
• Training allows the model to learn complex patterns in language.

What’s Next?

We now understand the basic idea behind language generation.

But LLMs can work with much more than simple text prediction. They can also follow instructions, answer questions, summarize information, and perform complex tasks.

In the next lesson, we will look at how AI models understand and follow instructions.