Foundational Concepts
Model
The finished AI system after training — the 'brain' that processes inputs and produces outputs.
Definition
In AI, a model refers to the trained system itself — the mathematical structure and learned weights that together define how the AI behaves. When people refer to 'using a model,' they mean interacting with this trained system by passing in an input (a prompt or piece of data) and receiving an output (a response or prediction). Different models have different strengths, limitations, and costs, which is why choosing the right model for a task matters.
Related Terms
Parameters
The internal numerical values a model adjusts during training — more parameters generally means more capable.
Foundation Model
A large AI model trained on broad data that can be adapted for many specific tasks.
Inference
The process of a trained AI model actually running and producing outputs — as opposed to being trained.
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Disclaimer
This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.
Document reference: ISO_webpage_knowledge-base_glossary_v1
Last modified: 29 March 2026