Q: What is fine-tuning?

Fine-tuning means taking an existing model and training it further on your own examples, so that the model itself changes.

That's the key difference from everything else on this page. Prompting and RAG don't touch the model. They change what you send to it. Fine-tuning changes the model's internal weights. You prepare thousands of example inputs and outputs, run a training job, and get back a modified version of the model that behaves more like your examples.

Here's the part most people get wrong: fine-tuning is not typically an AI Engineer's job. It sits in ML engineering and model-creation territory: training data curation, training runs, evaluating the resulting model. That's the science track.

So why learn what it is? Two reasons. First, "should we fine-tune?" comes up in almost every company, and the AI Engineer is the person expected to answer. Usually the right answer is no, or not yet. Second, interviewers ask about it to test whether you know the boundaries of the toolbox. Managed fine-tuning services have made small jobs easier, so you might run one someday. But if your daily work is training and tuning models, you're doing a different job than AI engineering.