What Is AI Training? A Plain-Language Explanation

When someone says an AI model was "trained," what does that actually mean?

Training is the process by which an AI system learns from data. It is analogous to how a person learns a skill, through repeated exposure to examples, feedback on performance, and gradual improvement over time. Except AI does this billions of times, in hours or days, on computers specifically designed for the task.

The basic process

Start with a large dataset, for a language model, this might be hundreds of billions of words of text from the internet, books, and other sources. Feed this data through the neural network. Compare the network's outputs to the correct answers. Adjust the network's internal parameters slightly to make it perform better. Repeat, billions of times.

By the end of training, the network has encoded patterns from all that data in its parameters, billions of numerical values that collectively represent what the model has learned.

Fine-tuning

After initial training, models are often fine-tuned on smaller, more specific datasets to make them better at particular tasks or to align their outputs with desired behaviour. This is how a general-purpose language model becomes specifically good at coding, or at following instructions, or at a particular tone of communication.

What "training an AI on your data" actually means for business

When companies talk about training AI on their own data, they usually mean one of two things. Fine-tuning an existing model on company-specific content to make it more relevant to their domain. Or, more commonly, using RAG, connecting an existing model to their documents without changing the model itself. The second is simpler, faster, and sufficient for most business needs.

Training a model from scratch is expensive and requires specialised expertise. For virtually all business applications, working with existing models is the right approach.