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Data & AI

An LLM is a type of AI model trained on massive text datasets to understand and generate human language, capable of tasks such as question answering, summarisation, code generation, and conversational dialogue.

LLMs are built on the Transformer neural network architecture and trained via self-supervised learning on trillions of tokens of text, allowing them to develop broad linguistic and factual knowledge without task-specific labelling. The scale of these models — billions to hundreds of billions of parameters — enables emergent capabilities that smaller models lack, such as multi-step reasoning, instruction following, and few-shot learning from examples in the prompt. Businesses deploy LLMs through hosted APIs (e.g., OpenAI, Anthropic, Google Gemini) or by fine-tuning open-source models (Llama, Mistral) on proprietary data for domain-specific applications. Key considerations for production LLM deployment include prompt engineering, retrieval-augmented generation (RAG) for grounding responses in current data, latency, cost per token, and output reliability.

Example

A legal tech company builds a contract review tool by fine-tuning an open-source LLM on thousands of annotated legal documents, enabling it to flag non-standard clauses with higher accuracy than general-purpose models.

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