What Is a Large Language Model (LLM)?
By Karthick Selvam · Published
A large language model is a neural network trained on large amounts of text to predict the next token, which lets it generate and understand language.
How it works, simply
Text is split into tokens. During training the model learns to predict the next token in a sequence across enormous text collections. This simple objective, at scale, produces useful abilities like summarisation, translation and reasoning-like behaviour.
The transformer
Most modern LLMs use the transformer architecture, whose attention mechanism lets the model weigh relationships between all tokens in its context.
Strengths and weaknesses
LLMs are flexible and fluent but can "hallucinate" incorrect facts, are limited by their context window and training data, and can reflect biases in that data.
Using LLMs in business
Common patterns are direct prompting, retrieval-augmented generation over company documents, fine-tuning for specialised tasks, and agents that call tools.
Frequently asked questions
What is a token?
A chunk of text, often part of a word. Model limits and pricing are usually expressed in tokens.
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- Multi-Agent Systems: Design & Orchestration
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