01In plain English
Fine-tuning takes a model that already works and trains it a little more on a set of examples you provide, such as your brand's writing or your product photos. The result is a version that behaves more like your examples.
02How it works
You start with a model that already writes or draws well. Then you prepare a set of examples that show what you want: for a writing model, pairs of a request and the ideal answer, and for an image model, a batch of pictures in your style. The tool runs more training on those examples, nudging the model so it behaves more like them. This takes a small fraction of the effort of building a model, but it still needs enough good examples and some testing afterward. The result is a private version that carries your tone or look every time without long instructions. It works like taking an experienced illustrator and showing them fifty of your brand pictures until they draw in your house style.
03Why it matters when you are choosing
It can give more consistent output than instructions alone, but it takes good examples, time and money. For many jobs, a clear prompt or a tool that reads your documents does the same for less.
04What to check
Check who owns the tuned model, where your training data goes, and whether retraining costs extra.
05Where you will meet it
06Common questions
When is fine-tuning worth it?
It pays off when you need a consistent style or format across a large volume of output and prompts alone do not get you there. For most small jobs, a clear prompt or a tool that reads your documents is cheaper and faster than training.
How many examples do I need to fine-tune a model?
It varies. Style training for images often works with a few dozen good pictures, while teaching a language model a task can need hundreds or thousands of examples. Quality matters more than quantity, since the model copies whatever the examples show, including their flaws.
Will fine-tuning teach the model facts?
Not reliably. Training changes how a model writes and behaves more than what it knows, and facts it picks up can be patchy. For answers that must be accurate and current, connect the model to your documents instead, using retrieval.