Model training: definition

Model training is the process of teaching an AI model by showing it data and adjusting its internal parameters until it performs the task well, which is done once up front and is computationally expensive.

Training is where a model learns. It is shown vast amounts of data and its millions or billions of parameters are tuned until it reliably produces good output. It happens once, takes serious computing power, and produces the finished model you then use.

Most businesses never train a model from scratch; they use a pre-trained one and, at most, fine-tune it on their own examples. Knowing the difference between training (build the model) and inference (use it) clears up a lot of cost confusion.

Related terms

Fine-tuning: Fine-tuning is further training a base model on your own examples so it learns a specific style, format, or task more reliably than prompting alone can achieve.
Inference: Inference is the act of running a trained model to get an output, and inference cost is what you pay each time the model answers, measured in tokens.
Neural network: A neural network is a computing system loosely inspired by the brain, made of layers of connected nodes that adjust their strengths as they learn from data.