Large Language Model (LLM)
A Large Language Model (LLM) is an artificial intelligence model trained on vast amounts of text data that can understand, generate, and reason about human language with remarkable fluency.
Understanding Large Language Model (LLM)
LLMs like GPT-4, Claude, and Gemini are neural networks with billions of parameters trained on diverse text from the internet, books, and other sources. They develop an understanding of language patterns, factual knowledge, and reasoning capabilities. LLMs can generate text, answer questions, summarize documents, write code, and engage in multi-step reasoning. In the context of AI agents, LLMs serve as the reasoning engine that decides what actions to take, how to interpret information, and how to communicate with users. They are the brain of modern AI systems.
How GAIA Uses Large Language Model (LLM)
GAIA uses LLMs as the reasoning engine for its AI agents. The LLM reads your emails, understands their content, decides what actions to take, drafts replies in your communication style, and orchestrates multi-step workflows.
Related Concepts
AI Agent
An AI agent is an autonomous software system that perceives its environment, reasons about what to do, and takes actions to achieve specific goals without continuous human direction.
LangGraph
LangGraph is a framework for building stateful, multi-agent AI applications that supports complex workflows with cycles, branching, conditional logic, and persistent state management.
Embeddings
Embeddings are dense numerical vector representations of data, such as text, images, or audio, that capture semantic meaning and relationships in a high-dimensional space.
AI Orchestration
AI orchestration is the coordination of multiple AI agents, models, and tools to work together in completing complex, multi-step tasks that no single component could handle alone.
System Prompt
A system prompt is a set of instructions provided to a language model at the beginning of a session that defines its persona, constraints, tone, and behavioral guidelines before any user interaction begins.
Temperature (AI)
Temperature is a parameter in language model inference that controls the randomness of token selection — higher temperatures produce more varied and creative outputs, while lower temperatures produce more consistent and deterministic responses.
Token
In AI, a token is the basic unit of text that language models process — roughly equivalent to 4 characters or ¾ of an average English word. Tokens are used to measure context window capacity and determine API usage costs.
Reasoning Model
A reasoning model is an AI language model specifically optimized to think through problems step-by-step using extended internal deliberation before producing a final answer, achieving higher accuracy on complex reasoning tasks.
Reinforcement Learning
Reinforcement learning (RL) is a machine learning paradigm in which an agent learns to make decisions by receiving reward signals for actions that achieve desired outcomes and penalty signals for undesired ones.
Transfer Learning
Transfer learning in AI is a technique where a model trained on one task reuses what it learned as the starting point for a different but related task, instead of training from scratch. It is the reason large language models are practical: they are pre-trained once on huge amounts of text, then adapted to chat, coding, or a specific domain with a small fraction of the data and compute.
Local LLM
A local LLM is a large language model that runs entirely on your own hardware — a laptop, workstation, or self-hosted server — without sending data to external API providers.
