When a business wants an AI model to behave in a particular way for its trade or its process, there are two very different technical routes: fine-tuning, which changes the model itself, and prompting, which instructs it each time through the context supplied.
What fine-tuning is
Fine-tuning means retraining an existing model on a specific body of data, altering its internal parameters so it "learns" particular behaviours permanently. It requires a significant quantity of example data and computing resources, and the process has to be repeated whenever you want to change the behaviour.
What advanced prompting is
Prompting does not change the model: it supplies, in every conversation, detailed instructions and relevant context — company data, examples of the behaviour wanted — that steer the answer without any permanent modification. It is more flexible and immediate to update, but requires the context to be rebuilt at each interaction.
Which suits a small business
For most small firms, well-designed prompting — with company context always current and clear instructions — offers a better cost-benefit balance than fine-tuning: it updates in real time, needs no dedicated training dataset, and adapts immediately to changes in company data. Fine-tuning remains useful for very specific needs that stay stable over time, which are less common in a small business.