An AI model learns patterns from training data and represents those patterns in parameters that are used during inference. Depending on its design, the model can classify information, retrieve or rank candidates, reason over a problem, generate language or code, or support another specialized task.
Models differ in capability, latency, cost, privacy characteristics, deployment options, and reliability. Those differences mean that a model should be evaluated in the context of the workload it will perform, rather than treated as universally better or worse than every alternative.
ELI5
An AI model is a trained computer system that has learned patterns from examples and uses those patterns to turn new inputs into outputs. Depending on its design, it may classify information, predict values, generate language or images, rank choices, or help control a larger application.
For example, a language model can receive a question and generate an answer based on learned token patterns and the supplied context. Different models vary in skill, cost, speed, privacy, and reliability, so each one should be tested on the real job it will perform.
How does an AI model produce an answer?
It applies learned parameters to an input during inference and calculates an output according to its architecture and training objective.
Is the most capable AI model always the best choice?
No. A less capable model can be preferable when it is faster, cheaper, easier to host, more private, or sufficiently reliable for the task.
