Fine-tune the model to make it accurate.

Mostly wrong. Teams reach for fine-tuning to fix wrong answers. Usually it's the slowest, most expensive way to solve a problem the prompt could have.

When an AI feature keeps getting things wrong, there's a popular next step. Fine-tune it. Train it on our data and it will finally be accurate. It sounds serious and technical, which is part of the appeal. It's also usually the wrong tool.