Large Language Models
From billions of parameters to boundless patterns; The systems that turned prediction into conversation
Large Language Models, or LLMs, are neural networks trained on massive text datasets to predict the next token. On the surface, it’s a simple goal — but with enough data, scale, and compute, it becomes a system that can write, reason, and solve problems across domains.
In the most simplest term, a large language model is a sophisticated mathematical function that predicts what word comes next for any piece of text.
What an LLM actually does?
Imagine you find a script for a movie scene between a person and their AI assistant — but all of the AI’s dialogue is missing.
If you had a machine that could look at the script so far and predict the next word the AI might say, you could rebuild the conversation word by word.
For example, let's say the user asks a simple prompt "How's the weather today?"
Notice how in the playground below, the language model replies/predicts the output token by token.
Playground: Language Models
Interactive
Interactions: Click on either play, step or rest and observe how the words are being predicted.
This is exactly how a large language model (LLM) works. It’s a mathematical function trained to predict the next token — a word, part of a word, or punctuation — based on the text that came before.
Instead of choosing one word with certainty, it assigns probabilities to all possible next words and picks one according to those probabilities.
Why is it called "Large"?
Because these models require staggering amounts of data and parameters. "Large" refers not just to their billions of learned weights, but also to the enormous text corpora used to train them - so big that a human could not hope to read it all.
Playground: Take a guess
Interactive
Interactions: Drop in what your guess is and submit to reveal the answer
How long would it take?
for a human to read all of the data GPT 3.5 was trained on,
continuously without a break every day
Modern LLMs use the transformer architecture, which reads tokens in parallel. The key ingredient is attention, a mechanism that lets the model focus on the most relevant parts of the text.
How attention resolves ambiguity
Words can have multiple meanings. Take the word bank — it might mean a financial institution or the side of a river. Attention lets the model weigh the context around the word and adjust its understanding on the fly.
In each case, attention highlights the surrounding words — water vs paycheck — to choose the right meaning.
This is just the intro. Next, we’ll cover prompt design and fine‑tuning. Later modules dig into retrieval augmentation and tool use.