How Large Language Models Are Trained: From Text to “Intelligence”

Large Language Models (LLMs) can write essays, explain economics, generate code, and hold conversations.

But underneath all of that, they’re doing something surprisingly simple.

They’re predicting the next word.

How does that translate into something that feels intelligent?

Let’s break it down.

Step 1: Learning From Huge Amounts of Text

LLMs are trained on massive datasets of text, including:

• Books

• Articles

• Websites

• Public documents

• Code repositories

  
  

The goal isn’t to memorise facts — it’s to learn patterns in language:

• Grammar

• Style

• Structure

• Relationships between words and ideas

  
  

At this stage, the model doesn’t “understand” anything. It only sees sequences of words and learns what tends to follow.

Step 2: The Core Task — Next-Word Prediction

Training starts with a simple game:

Given this text, what’s the most likely next word?

For example:

“The UK economy is experiencing high ___”

The model guesses a word.

• If it’s wrong, it’s penalised

• If it’s right (or close), it’s rewarded

  
  

This happens billions of times.

Over time, the model gets very good at predicting:

• Not just the next word

• But the next proper word, in context

  
  

This is where complexity emerges from simplicity.

Step 3: Neural Networks and Weights

LLMs are built using neural networks with:

• Millions or billions of parameters (called weights)

• Layers that transform input text into representations

  
  

During training:

• Weights are adjusted slightly each time

• Tiny improvements compound over time

  
  

Eventually, the model encodes:

• Facts

• Reasoning patterns

• Writing styles

• Even abstract concepts

  
  

Not because it was told to them, but because it was statistically valid.

Step 4: Fine-Tuning for Helpfulness and Safety

A raw language model isn’t delightful to talk to.

So after initial training, models are fine-tuned using:

• Human feedback

• Example answers

• Preference rankings

  
  

Humans show the model:

• What good answers look like

• What bad or unsafe answers look like

  
  

This process teaches the model to be:

• More helpful

• More polite

• More aligned with human expectations

  
  

This stage is crucial for making models usable in the real world.

Step 5: What LLMs Are (and Aren’t) Doing

LLMs are not:

• Conscious

• Thinking like humans

• Reasoning symbolically in the traditional sense

  
  

They are:

• Extremely advanced pattern recognisers

• Probability engines over language

• Able to simulate reasoning by chaining patterns

  
  

When an LLM explains something well, it’s because explanations look a certain way in the data it learned from — and it learned that structure.

Why Training LLMs So Expensive:

Training requires:

• Enormous computing power

• Huge datasets

• Massive energy consumption

  
  

This is why only a handful of organisations (like OpenAI) can train frontier-level models.

Once trained, models are far cheaper to run than to create.

Why Should You Care?

LLMs show how:

• Intelligence can emerge from data and scale

• Simple objectives can lead to complex behaviour

• Technology reshapes how we learn, write, and work

  
  

They also raise significant questions about:

• Education

• Creativity

• Labour markets

• Trust and misinformation

  
  

Understanding how they’re trained helps separate real capability from hype.

The Big Picture

LLMs aren’t magic — but they are powerful.

They’re trained by:

1. Consuming vast amounts of text

2. Learning to predict language

3. Being shaped by human feedback

   
   

The result isn’t human intelligence, but a tool that can mirror, remix, and reason through language at an unprecedented scale.