Training is the phase during which an AI model learns from data, gradually adjusting its internal parameters to reduce its errors on a given task.
The principle: the model is shown a large number of examples, the gap between its answer and the expected answer is measured, then its parameters are corrected in the right direction. The operation is repeated millions of times. For a large language model, a distinction is made between pre-training (learning language on a massive corpus) and later stages such as fine-tuning or RLHF, which steer it toward a use case.
Training is the opposite of inference, which means using the model once it has learned. Pre-training a foundation model requires GPU clusters and highly specialized teams; most companies simply adapt an existing model.
Why it matters when hiring
Few companies actually train models from scratch. Before looking for a "training expert", check the need: adapting an open model and pre-training a foundation model call for different profiles. The good signal: having run distributed training across several GPUs, handled instabilities and dirty data. The trap: a resume that lists frameworks without any measured result.