An LLM, or Large Language Model, is a type of artificial intelligence model designed to understand, generate, and manipulate human language at scale. LLMs are trained on vast amounts of text data and can perform tasks such as answering questions, writing content, summarising documents, translating languages, and generating code.
Large language models sit at the core of many modern AI applications, including chatbots, virtual assistants, search tools, and generative AI systems. Their ability to process and generate natural language makes them a foundational technology for human-AI interaction.
What is an LLM?
A large language model is a machine learning model trained to predict and generate text based on context. Given an input prompt, an LLM produces a sequence of words that statistically aligns with patterns learned during training.
The term “large” refers both to the size of the model, often containing billions of parameters, and the scale of the data used during training. This scale allows LLMs to capture complex linguistic patterns, contextual relationships, and general knowledge across many domains.
Unlike traditional rule-based language systems, LLMs do not rely on predefined grammar rules. Instead, they learn language structure implicitly from data.
How do large language models work?
LLMs are built using deep learning techniques, most commonly neural network architectures designed for sequential data. During training, the model is exposed to large corpora of text and learns to predict the next word (or token) in a sequence based on surrounding context.
Over time, this training process enables the model to generate coherent sentences, maintain context across longer passages, and respond appropriately to a wide variety of prompts.
Once trained, an LLM can be prompted with natural language instructions. It then uses probability distributions over tokens to generate responses that are contextually relevant and linguistically fluent.
What can LLMs do?
Large language models support a wide range of language-based tasks. These include text generation, summarisation, translation, sentiment analysis, question answering, and conversational interaction. These tasks fall under the umbrella of Generative AI↗, i.e. AI that is designed and trained specifically for the purpose of generating content.
LLMs are also capable of reasoning-like behaviours, such as following instructions, completing multi-step tasks, or generating structured outputs like tables or code snippets. While they do not “understand” language in a human sense, their ability to model linguistic patterns makes them highly effective across many use cases.
Because LLMs are general-purpose, the same model can often be applied to many different tasks with minimal additional training.
LLMs vs traditional NLP models
Traditional natural language processing (NLP) models are typically designed for specific tasks, such as classification or keyword extraction, and rely on manually engineered features or smaller datasets.
LLMs differ in both scale and flexibility. They are trained once on massive datasets and can then be adapted to many tasks through prompting or fine-tuning. This reduces the need for task-specific model development and enables more natural interaction through plain language.
As a result, LLMs have largely replaced older NLP approaches in many applications.
LLMs and generative AI
LLMs are a core component of generative AI, particularly for text-based generation. When people refer to generative AI systems that write text, answer questions, or engage in conversation, they are often referring to applications powered by large language models.
However, not all generative AI systems are LLMs. Image, audio, and video generation rely on different model types, though many modern systems combine LLMs with other models to create multimodal capabilities.
Use cases for LLMs
LLMs are used across consumer, enterprise, and developer-facing applications.
In consumer products, LLMs power chat interfaces, writing assistants, and search experiences. In business environments, they support customer support automation, document analysis, internal knowledge systems, and reporting.
Developers use LLMs to assist with code generation, debugging, documentation, and software prototyping. Because large language models can interpret both natural language and programming languages, they are particularly well suited to developer workflows.
LLMs in business and operations
Within organisations, LLMs are increasingly embedded into tools that help employees access information, generate content, and automate routine tasks.
In operational and finance contexts, LLMs may assist with interpreting policies, summarising documents, extracting insights from unstructured data, or supporting workflows such as invoice coding, processing or reporting. Rather than replacing core systems, LLMs often act as an intelligent layer that makes existing data and tools easier to use.
Their value lies in reducing friction between humans and complex systems.
Key characteristics of LLMs
Large language models are defined by several key characteristics. They are highly scalable, capable of handling diverse inputs, and adaptable across tasks. They generate outputs probabilistically rather than deterministically, which allows flexibility but also introduces variability.
LLMs are also context-sensitive, meaning their outputs depend heavily on the input prompt. Prompt design plays a significant role in determining response quality, accuracy, and usefulness.
Limitations and risks of LLMs
Despite their capabilities, large language models have important limitations. They may produce incorrect or misleading information, especially when prompted about unfamiliar or ambiguous topics. Because they generate text based on probability rather than verified facts, outputs should not be treated as inherently authoritative.
LLMs can also reflect biases present in their training data and may struggle with transparency or explainability. Data privacy, intellectual property, and security considerations are especially important when LLMs are used with sensitive or proprietary information.
For these reasons, LLMs are often deployed with human oversight and clear usage guidelines.
Governance and responsible use of LLMs
Responsible use of large language models requires governance frameworks that define acceptable use, access controls, and review processes. Organisations typically establish rules around what data can be shared with LLMs, how outputs are validated, and when human approval is required.
Logging, monitoring, and auditability are also important, particularly in regulated industries. Governance helps ensure that large language models support productivity without introducing unacceptable risk.
The future of LLMs
LLMs are evolving rapidly, with improvements in model efficiency, context length, multimodal capabilities, and integration into software systems. Future developments are likely to make LLMs more accurate, controllable, and specialised for domain-specific tasks.
Rather than acting as standalone tools, LLMs are increasingly embedded into broader AI systems, including AI agents and enterprise platforms. As this integration deepens, LLMs will continue to shape how people interact with technology.
Summary
An LLM (Large Language Model) is an AI system trained on massive amounts of text to understand and generate human language. LLMs underpin many modern generative AI applications and enable flexible, natural interaction with software systems. While powerful, they require careful implementation, oversight, and governance to ensure accuracy, security, and responsible use.