Lesson 99: Pretraining Large Language Models

Before a Large Language Model becomes useful for chat, coding, or reasoning, it must first learn how language works at a very large scale. This initial learning phase is called pretraining.

In this lesson, you will understand what pretraining is, how it works, what data is used, and why this phase is essential for all LLMs.

What Is Pretraining?

Pretraining is the process where an LLM learns general language patterns from massive text data without being trained for a specific task.

During pretraining, the model does not know about chatbots, coding assistants, or question answering. It only learns how language behaves.

• Grammar and sentence structure
• Word relationships and meaning
• Common facts and patterns

Real-World Analogy

Think of pretraining like learning a language by reading millions of books, articles, and conversations. You are not trained to answer exams yet, but you understand how the language works.

Fine-tuning comes later, just like exam preparation.

What Data Is Used for Pretraining?

LLMs are pretrained on extremely large and diverse datasets.

• Web pages
• Books and articles
• Code repositories
• Technical documentation

The goal is to expose the model to as much language variety as possible.

Pretraining Objective: Next Token Prediction

The main objective during pretraining is simple: predict the next token given previous tokens.

For example:

"Machine learning is a branch of"

The model learns to predict likely next tokens such as "artificial" or "computer".

Pretraining Flow (Conceptual Code)

text = load_large_corpus()

tokens = tokenize(text)

for batch in tokens:
    predictions = model(batch)
    loss = compute_loss(predictions, batch)
    update_model(loss)
  

This loop repeats billions of times during pretraining, allowing the model to gradually learn language patterns.

Loss Function in Pretraining

The model uses a loss function to measure how wrong its predictions are. Lower loss means better predictions.

• High loss → poor predictions
• Low loss → accurate predictions

Training continues until the loss stabilizes at an acceptable level.

Why Pretraining Is So Expensive

Pretraining requires enormous computational resources.

• Thousands of GPUs or TPUs
• Weeks or months of training
• Large memory and storage

This is why only a few organizations can train very large models from scratch.

What Pretraining Does NOT Do

Pretraining alone does not make a model safe, helpful, or aligned.

• It does not follow instructions well
• It may generate unsafe outputs
• It lacks conversational behavior

These issues are addressed later through fine-tuning and alignment techniques.

Practice Questions

Quick Quiz

Coming up next: Fine-tuning LLMs — how pretrained models are adapted for specific tasks and behaviors.