How Do LLMs Work? Understanding Next Token Prediction

🚀 Introduction

Large Language Models (LLMs) are at the core of modern Generative AI systems. They can generate human-like text, answer questions, write code, and even assist in complex reasoning tasks. But despite how intelligent they may seem, the underlying mechanism behind these models is surprisingly simple.

In this post, we will break down how LLMs actually work and explore the key idea that powers them: next-token prediction.

🧠 What is an LLM?

A Large Language Model (LLM) is a deep learning model trained on massive amounts of text data to understand and generate human language. These models learn patterns, structures, and relationships between words, allowing them to produce coherent and context-aware text.

However, it is important to understand that LLMs do not “think” or “understand” language in the way humans do. Instead, they rely on statistical patterns learned from data.

🔑 The Core Idea: Next-Token Prediction

At the heart of every LLM lies a simple idea:

Predict the next most likely token.

A token can be a word, part of a word, or even a character. When you give an input to an LLM, the model does not generate the entire response at once. Instead, it builds the output step by step.

For example:

Input: The weather today is

The model evaluates possible next tokens:

“sunny” (0.45)
“great” (0.30)
“cold” (0.25)

It selects one (often the highest probability or sampled) and then continues: The weather today is sunny and

This process repeats until the model generates a complete response.

🔄 Step-by-Step Generation Process

The generation process follows a loop:

The model receives an input prompt
It predicts the next token based on probabilities
The predicted token is added to the input
The process repeats

This iterative process allows LLMs to generate long and coherent text sequences.

📊 Why Probability Matters

LLMs are fundamentally probabilistic models. Instead of choosing a single deterministic output, they assign probabilities to possible next tokens.

This is why the same prompt can produce different outputs depending on:

temperature
sampling strategy
randomness

This probabilistic nature is what makes LLMs flexible and creative.

🔢 From Text to Numbers: Embeddings

Before processing any text, the model converts words into numerical representations called embeddings.

Embeddings allow the model to:

represent words as vectors
capture semantic relationships
understand context

For example, the words “king” and “queen” will have similar vector representations, reflecting their related meanings.

⚙️ Training vs Inference

LLMs operate in two main phases:

Training

During training, the model learns from massive datasets by repeatedly predicting the next token and adjusting its internal parameters.

Inference

During inference, the model uses what it has learned to generate text based on user input.

When you interact with an AI system, you are experiencing the inference phase.

🤖 Does the Model Really Understand?

This is one of the most important questions.

LLMs:

do not have consciousness
do not truly understand meaning
do not reason like humans

Instead, they are extremely powerful pattern recognition systems that generate text based on learned probabilities.

Yet, because they are trained on massive datasets, their outputs often appear intelligent and meaningful.

🎯 Conclusion

Despite their complexity, the core idea behind LLMs is simple: predicting the next token based on probability.

By repeating this process, LLMs can generate coherent text, simulate conversations, and perform a wide range of language tasks.

Understanding this fundamental mechanism is essential before diving deeper into more advanced topics such as transformer architectures, attention mechanisms, and fine-tuning techniques.

🚀 In the next post, we will explore Transformer architecture and understand why it replaced older models like RNNs.