Why it matters for your business
Businesses often assume they need to train a model on their data, when retrieval and good prompts would solve the problem faster and more cheaply. Fine-tuning requires a clean, consistent set of examples, often starting at 50 to 100 and growing into the hundreds for harder tasks, and usually has to be redone to move to a newer base model. It makes sense for narrow, high-volume tasks where consistent format or tone matters, or where a smaller fine-tuned model could replace a larger, more expensive one. Try prompting and retrieval first, and measure with evals before deciding.
Example
An ecommerce brand generates thousands of product descriptions a month. Prompting a general model produced acceptable copy, but the tone drifted from the brand voice. The team fine-tunes a smaller model on several hundred descriptions written by its copywriters, then compares it against the prompted larger model on a review set before switching production traffic.