AI Fundamentals

Large Language Model (LLM)

A neural network trained on massive text data that can understand and generate human-like language, code, and reasoning.

modelsfoundationcore-concept

A large language model is the engine behind modern AI agents. Models like Claude, GPT-4, and Gemini are trained on billions of words of text and code, giving them the ability to understand context, follow instructions, write prose, generate code, and reason through complex problems.

LLMs work by predicting the next token (word or sub-word) in a sequence. Despite this simple mechanism, scale and training data create emergent capabilities: they can translate languages, write essays, debug code, and even plan multi-step projects. The "large" refers to parameter count — modern models have hundreds of billions of parameters.

For practical purposes, what matters is not how LLMs work internally but what they enable. An LLM is the brain of an AI agent. Pair it with tools (code execution, web browsing, file access) and you get an autonomous worker. The quality of the LLM directly determines the quality of the agent — which is why Agentik {OS} uses frontier models and upgrades as better ones become available.

Related Terms

Token

The basic unit of text that AI models process — roughly equivalent to a word or word fragment.

Context Window

The maximum amount of text an AI model can process in a single interaction, measured in tokens.

Inference

The process of running a trained AI model to generate predictions or outputs from new inputs.

Fine-Tuning

Training a pre-trained AI model on specialized data to improve its performance on specific tasks or domains.

Hallucination

When an AI model generates plausible-sounding but factually incorrect information with unwarranted confidence.

Want to see AI agents in action?

Book a Demo