The process of teaching a model by exposing it to data so it can learn patterns and behaviors.
Training is the process through which an AI model learns from data. It is what gives the model its general language ability, knowledge patterns, and response tendencies before any prompt is ever entered.
Prompt engineering does not usually change the underlying training of the model. Instead, it works with the model as trained and tries to guide behavior effectively at inference time.
Understanding training helps explain both the strengths and the limits of prompting. The model can only generalize from what it has learned, which is why some prompts succeed easily and others require examples, constraints, or external grounding.
Training data, architecture, and optimization methods all influence what the model knows, how it reasons, and what kinds of errors it is likely to make.