Large Language Models (LLMs)
The World's Most Avid Reader
8 min read
The World's Most Avid Reader
Imagine reading the entire internet — every book, blog, Wikipedia article, and forum post — and then being asked any question.
An LLM has done exactly that (almost). It read trillions of words and learned to predict what word comes next, millions of times over. Through this process, it picked up grammar, facts, reasoning, and even humour. It doesn't 'know' things the way you do — it predicts the most likely next word, over and over, until an answer appears.
In Plain English
An LLM is an AI trained on massive amounts of text to understand and generate human language. It works by predicting the next word based on all the previous words — do this billions of times and you get something that can write essays, answer questions, and code.
The Technical Picture
LLMs are Transformer-based neural networks trained on large text corpora using next-token prediction (autoregressive language modelling). The Transformer's attention mechanism allows the model to weigh relationships between all tokens in its context window simultaneously, enabling complex language understanding and generation.
Real-World Examples
- ChatGPT (GPT-4), Claude, and Gemini are all LLMs
- GitHub Copilot uses an LLM to complete your code
- Grammarly uses LLMs to suggest better phrasing
LLMs are autocomplete on steroids — trained on the entire internet to predict the perfect next word.