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What is it?
Training is the process by which an AI model is shaped through exposure to large amounts of data. The model repeatedly adjusts its internal parameters to recognise patterns and make predictions. For a language model such as GPT or Claude, that data consists primarily of text: books, websites, documents, and code.
Training is not the same as programming. A conventional software program runs on rules that a person has written; a trained AI model runs on patterns that it has derived from the examples in its training data. That distinction determines what AI can and cannot do.
Why it matters for SMEs
For an SME owner, training matters because it explains what an AI model knows, what it does not know, and why it sometimes falls short on specific or recent information.
- A model's knowledge extends only to the point at which its training ended: events, regulations, or product changes after that date are unknown to the model unless supplied through the context.
- A model trained predominantly on general English-language text performs less well on specific Dutch terminology, sector processes, or internal procedures, unless guided through fine-tuning or prompt instructions.
- The quality and diversity of the training data partly determine how reliable and balanced the model is: a model trained on one-sided sources carries a corresponding bias in its output.
Knowing that a model learns through training rather than rules helps set realistic expectations for what it can deliver and where human oversight remains necessary.
How it works
Training proceeds in iterations: the model makes a prediction based on the current data, compares it to the desired output, and adjusts its internal parameters to reduce the error. That process repeats billions of times across the training data.
- The training data is prepared and organised into input-output pairs or sequential text.
- The model makes a prediction for the next step or token based on the input.
- The gap between the prediction and the actual value is calculated as a loss function.
- Through backpropagation, the model's internal parameters are adjusted to reduce the error.
- This process repeats across the full dataset, multiple passes if necessary, until the model performs well enough.
After training, the model is tested on data it has not seen before to verify that it generalises rather than simply memorising its training examples. Large models such as GPT-4 or Claude go through this process on provider-scale infrastructure, not on a company's own hardware.
Example in practice
Picture a recruitment agency that wants an AI tool to write vacancy texts in the company's own house style. The agency collects a hundred well-regarded vacancy texts from recent years and uses them as fine-tuning data on top of an existing language model. After the training process, the model automatically writes in the right tone, with the usual structure and the terminology that the agency's clients recognise. The training has turned a generic model into a tool that fits the agency's identity.
Comparison and misconceptions
Training shapes the model from historical data and determines what it knows generically; fine-tuning is a targeted follow-on training on specific data to specialise the model. RAG (retrieval-augmented generation) adds current information through the context rather than retraining the model. For most SME applications, RAG is the practical choice; training and fine-tuning are for specialised situations.
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