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" part refers to the scale of both the training data and the model's internal parameters — the mathematical values that determine how the model processes and responds to input.
LLMs power many of the AI tools you interact with today: chatbots, writing assistants, code generators, and search enhancements. Understanding how they work helps you use them more effectively and think critically about their limitations.
The Foundation: Neural Networks
At their core, LLMs are built on a type of neural network called a transformer, introduced in a landmark 2017 research paper. Neural networks are loosely inspired by biological brains — they consist of layers of interconnected nodes that process information and adjust based on feedback during training.
What makes transformers special is an innovation called self-attention. Rather than processing text word by word in sequence, a transformer can look at all words in a piece of text simultaneously and learn which words are most relevant to each other — regardless of how far apart they are in the sentence.
How Training Works
Training an LLM involves feeding it enormous quantities of text — web pages, books, articles, code repositories, and more — and having it repeatedly try to predict the next word (or token) in a sequence. When it gets the prediction wrong, the model's parameters are adjusted slightly to do better next time. This process, called gradient descent, is repeated billions of times.
After this initial pre-training phase, most modern LLMs go through additional training steps:
- Supervised fine-tuning: The model is trained on curated examples of high-quality responses to make it more helpful and coherent.
- Reinforcement Learning from Human Feedback (RLHF): Human raters compare model outputs and indicate which are better. The model learns to produce responses that humans prefer.
What Are Tokens?
LLMs don't process text letter by letter or word by word — they work with tokens, which are chunks of text that can be a word, part of a word, or a punctuation mark. The word "unbelievable" might be split into three tokens: "un", "believ", "able". A typical English word is roughly 1–1.5 tokens.
Understanding tokens matters for practical reasons: models have a maximum context window — the number of tokens they can consider at once. If your input plus the desired output exceeds this limit, the model can't process the full conversation.
Why LLMs Sometimes Get Things Wrong
LLMs are pattern-matching engines, not databases with verified facts. Several types of errors are inherent to how they work:
- Hallucinations: The model generates plausible-sounding but incorrect information because it's optimizing for linguistic coherence, not factual accuracy.
- Knowledge cutoff: Models are trained on data up to a certain date and don't know about events after that cutoff unless given external tools.
- Context sensitivity: Slight rephrasing of a question can produce very different answers because the model's output depends on the statistical patterns in training data.
Key Concepts at a Glance
| Term | What It Means |
|---|---|
| Parameters | The numerical values inside the model that encode learned knowledge |
| Token | A chunk of text the model processes as a single unit |
| Context window | How much text the model can "see" at once |
| Transformer | The neural network architecture underlying most modern LLMs |
| RLHF | Training technique using human feedback to improve response quality |
| Hallucination | When a model confidently generates false information |
Where the Technology Is Heading
Researchers are actively working on making LLMs more factually reliable, more efficient to run, and better at reasoning through multi-step problems. Techniques like retrieval-augmented generation (RAG) — where the model is connected to a live database or search engine — are helping address the hallucination and knowledge cutoff problems.
As these systems become more capable, having a solid mental model of how they work will help you make better decisions about when to trust them, when to verify their outputs, and how to prompt them effectively.