What Is a Large Language Model?
A Large Language Model (LLM) is a type of artificial intelligence system trained to understand and generate human language. The "large" refers to the scale of both the model's parameters (the internal numerical values that encode its knowledge) and the training data used to develop it. Modern LLMs are trained on vast collections of text from books, websites, code repositories, and other sources — often hundreds of billions of words or more.
The result is a system that can engage in conversation, answer questions, write content, summarize documents, translate languages, and assist with coding — all from a single model architecture.
The Core Idea: Predicting the Next Token
At their heart, LLMs are trained to do one thing: predict what comes next in a sequence of text. During training, the model sees enormous amounts of text and learns to predict the next word (or more precisely, the next "token" — a chunk of characters) given everything that came before it.
Through this seemingly simple objective, applied at massive scale, the model develops an internal representation of grammar, facts, reasoning patterns, and even something resembling common sense. It's a counterintuitive result: predicting the next word, done well enough on enough data, produces a surprisingly capable general-purpose language system.
Key Components Explained
Tokens
LLMs don't process raw characters or whole words — they work with tokens. A token might be a whole word, part of a word, punctuation, or a space. "unbelievable" might be split into ["un", "believ", "able"]. This tokenization allows the model to handle any text, including rare words and multiple languages, without needing an infinitely large vocabulary.
The Transformer Architecture
Almost all modern LLMs are built on the Transformer architecture, introduced in a landmark 2017 paper. The key innovation is the attention mechanism, which allows the model to weigh the relevance of different parts of the input when generating each output token. This means the model can understand long-range dependencies in text — connecting a pronoun to its referent many sentences earlier, for example.
Parameters
Parameters are the numerical values inside the model that are adjusted during training. A model with more parameters has more capacity to store patterns and nuance. Parameter counts are often cited to describe model size:
- Small models: Under 7 billion parameters — can run on consumer hardware
- Mid-size models: 7B–70B parameters — balance capability and resource requirements
- Large models: 70B+ parameters — typically require significant compute to run
Training vs. Inference
Training is the computationally intensive process of adjusting the model's parameters using massive datasets. It happens once (per model version) and requires specialized hardware clusters. Inference is when the trained model generates responses to your inputs — this is what happens every time you use ChatGPT or a similar tool.
What LLMs Can and Can't Do
| LLMs Are Good At | LLMs Struggle With |
|---|---|
| Generating fluent, coherent text | Precise arithmetic and counting |
| Summarizing and reformatting information | Verifying facts in real-time |
| Explaining concepts | Knowing current events (without tools) |
| Writing and debugging code | Consistent logical reasoning on complex chains |
| Creative writing and brainstorming | Citing sources reliably |
The Concept of "Hallucination"
LLMs sometimes generate confident-sounding but factually incorrect information — a phenomenon commonly called hallucination. This happens because the model is optimized to produce fluent, plausible text, not necessarily true text. It doesn't have a separate fact-checking mechanism unless one is explicitly built in. This is why LLM outputs should always be verified for high-stakes factual claims.
Why This Matters
Understanding how LLMs work helps you use them more effectively and critically. Knowing that they predict tokens rather than "think" helps explain both their impressive capabilities and their characteristic failure modes. As these tools become embedded in software, workplaces, and daily life, a basic literacy in what they are — and aren't — is increasingly valuable.