How Large Language Models Work

How Large Language Models Work

An interactive journey through the core mechanics behind AI's most powerful text generation tools

1
Data Collection
2
Tokenization
3
Training
4
Inference
5
Prediction
6
Summary

STEP 1: DATA COLLECTION

LLMs learn from vast amounts of text data from across the internet and published works

Books & Articles

Websites

Code

Scientific Data

Trillions of words are collected to train modern LLMs:

Models like GPT-4 and Claude are trained on hundreds of billions of tokens from diverse sources

STEP 2: TOKENIZATION

Text is broken down into smaller pieces called "tokens"

A tokenizer converts text into numerical values the model can understand:

Artificial intelligence is transforming how we work.

Tokens can be words, parts of words, or even single characters, depending on the tokenization algorithm

STEP 3: TRAINING

The model learns patterns by predicting the next token in sequences

Training involves adjusting billions of parameters to minimize prediction errors

STEP 4: INFERENCE

When given a prompt, the model generates text one token at a time

User prompt:

Write a short tagline for AI Mindset's course.

The model uses what it learned during training to generate appropriate responses

STEP 5: TOKEN PREDICTION

The model predicts the next token based on probability distributions

For the prompt "AI will help us..."

The model calculates the probability of each possible next token:

The model may choose the highest probability token or sample from the distribution for creativity

HOW LLMS WORK: SUMMARY

Key concepts behind large language models

  • LLMs learn patterns from massive datasets of text collected from books, websites, and more
  • Text is broken into tokens that the model can process mathematically
  • During training, the model learns to predict what comes next in a sequence
  • When you provide a prompt, the model generates one token at a time
  • Each new token is predicted based on the context of all previous tokens
  • The model has no true understanding—it predicts patterns based on statistical relationships

Understanding these mechanics helps use LLMs more effectively in your work

AI Mindset Footer Navigation

Explore Our Resources