Large Language Model (LLM)
Quick Answer
A Large Language Model (LLM) is a type of neural network trained on vast quantities of text to understand and generate human language. LLMs power chatbots, copilots, content generators and many modern AI features across consumer and business software.
In Depth
What Large Language Model (LLM) really means
LLMs are typically transformer-based architectures trained on trillions of tokens scraped from the web, books, code repositories and other text sources. During training they learn statistical patterns of language, which enables them to complete prompts, summarise documents, translate, and answer questions.
Despite their impressive fluency, LLMs do not 'understand' in a human sense. They generate plausible continuations based on learned patterns, which is why fact-checking, retrieval augmentation and guardrails remain essential for reliable enterprise use.
Why It Matters
Business relevance for UK organisations
LLMs are the single most transformative AI technology for UK knowledge workers. They are already embedded in email, spreadsheets, CRM, helpdesks and development tools. The competitive question is no longer whether to use LLMs, but how to deploy them safely and effectively.
Real-world example
How this shows up in practice
A Cambridge law firm deployed an internal LLM-based assistant to draft first-pass responses to client queries, saving an average of 11 hours per solicitor per week.
Related Terms
Continue exploring
Transformer
The transformer is a neural network architecture introduced in 2017 that uses a mechanism called self-attention to process sequences in parallel. It is the foundational architecture behind nearly all modern large language models and many leading vision and audio models.
TechnicalToken
A token is the basic unit of text that a language model processes. Tokens are usually subword chunks — roughly four characters or three-quarters of a word in English — and both the size of the model's context window and its pricing are typically measured in tokens.
TechnicalPrompt Engineering
Prompt engineering is the practice of designing the text instructions given to a language model to produce reliable, accurate and appropriate outputs. Good prompts unlock significantly better performance without any change to the underlying model.
TechnicalHallucination
A hallucination is when an AI model produces output that sounds plausible but is factually incorrect, fabricated or inconsistent with its sources. Hallucinations are a fundamental property of current generative models and the single biggest risk in enterprise deployments.
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