Large Language Model (LLM)

A large language model is an AI model trained on very large amounts of text to predict and generate language. It can read, summarise, classify, translate, and draft text, and can power chatbots and AI agents. LLMs are flexible but can make confident mistakes, so outputs need verification.

A large language model (LLM) learns patterns in language by training on large text datasets, then uses those patterns to generate the next words in a response. Examples include the GPT, Claude, and Gemini families. Because language underpins so much business work, one LLM can handle many tasks — summarising, classifying, extracting data, drafting, and answering questions.

LLMs are powerful but not infallible. They can produce fluent answers that are wrong, a behaviour often called hallucination, and they only know what was in their training data unless you give them more. That is why production systems pair LLMs with grounding techniques like retrieval-augmented generation, validation, and human review.

Why it matters

An LLM is the reasoning engine inside most AI agents and AI workflows.
Reliability comes from how you use the model — grounding, guardrails, and checks — not the model alone.

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