LLM (Large Language Model)
A large language model is an AI system trained on massive text datasets that can understand and generate human language. Examples include GPT-4, Claude, Llama, and Mistral.
What is an LLM?
A large language model (LLM) is a type of artificial intelligence system built on transformer architecture and trained on billions of text examples. The 'large' refers to the model's parameter count — modern LLMs have hundreds of billions of parameters that encode statistical patterns about language.
LLMs can perform a wide range of language tasks without being explicitly programmed for each one: summarizing documents, answering questions, translating languages, writing code, generating reports, and extracting structured information from unstructured text.
How LLMs work
During training, the model processes enormous quantities of text — books, websites, scientific papers, code repositories — and learns to predict what word comes next in a sequence. Through this process, the model develops internal representations of concepts, facts, and reasoning patterns.
At inference time (when you use the model), you provide a prompt, and the model generates a response by predicting the most likely continuation given its training and your input. The quality of the output depends on the model size, training data quality, and how the prompt is structured.
Private LLMs vs. public LLMs
Public LLMs (like ChatGPT or Claude.ai accessed through a browser) process your inputs on the provider's servers. Private LLMs are deployed within your own infrastructure, ensuring your data never leaves your control. For enterprises handling sensitive data, private deployment is often required by GDPR obligations or contractual commitments to clients.
Frequently asked questions
What's the difference between an LLM and ChatGPT?
ChatGPT is a product built by OpenAI on top of their GPT family of LLMs. An LLM is the underlying model technology. ChatGPT is one specific application; LLMs are the general technology class that powers it and many similar tools.
What does it mean to deploy an LLM privately?
Private deployment means running the LLM on your own servers or cloud environment. Your data — the queries, documents, and context you provide — never leaves your infrastructure and is never processed by a third-party provider.
How large does an LLM need to be for enterprise use?
Model size is not the only factor. A smaller, well-fine-tuned model (7B-13B parameters) can outperform a larger general model on specific enterprise tasks. The right size depends on your use case, hardware constraints, and required response speed.
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