LLM (Large Language Model)
An LLM (Large Language Model) is an AI system trained on massive amounts of text to predict and generate human language. It can answer questions, write, summarise, translate, and reason by learning statistical patterns across billions of examples.
In plain English
An LLM is like an extremely well-read autocomplete. It has read an enormous slice of the internet and learned which words tend to follow which — good enough that it can hold a conversation, draft an email, or write code.
How it works
- 1The model is trained on huge text datasets to predict the next token (word-piece) in a sequence.
- 2Through that training it learns grammar, facts, reasoning patterns, and style.
- 3At use time, you send a prompt and it generates a response token by token.
- 4Techniques like RAG, tools, and fine-tuning extend what it can reliably do.
Claude, GPT, and Gemini are LLMs. Ask one to 'summarise this contract in three bullet points' and it produces a fluent, structured answer from your text.
Why it matters
LLMs power chatbots, copilots, summarisation, classification, and automation. If a task involves understanding or producing natural language at scale, an LLM is usually the engine behind it.
Related
LLM — FAQs
What's the difference between an LLM and AI?
AI is the broad field; an LLM is one type of AI specialised in language. All LLMs are AI, but not all AI is an LLM (image recognition, for example, isn't).
Which LLM is best?
It depends on the task, budget, and constraints. We're model-agnostic and choose per job — often Claude — backing the choice with evaluations rather than hype.
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