Taking a capable general model and training it a little more on your own examples to make it better at one specific job.
Pretraining builds a broadly capable model at great expense. Fine-tuning takes that finished model and trains it a bit further on a smaller set of examples, to specialize it for a particular task, tone, or format.
It is the difference between hiring a smart generalist and giving them a week of on-the-job training for your specific role. The raw ability was already there; fine-tuning points it in a direction.
You collect examples of the behavior you want, often input and ideal-output pairs, and continue the training process on just those. The parameters shift to favor that pattern. Because you start from an already capable model, it takes far less data and compute than training from scratch.
Fine-tuning excels at shaping form and behavior: matching a house style, always replying in a fixed structure, handling a narrow domain's phrasing, or being consistent at a specialized task. If you need the model to reliably act a certain way, fine-tuning is a strong tool.
It is a poor way to add facts. Fine-tuning teaches patterns, not a reliable, updatable knowledge base, and baked-in facts go stale the moment they change. For "know this information", retrieval that fetches the facts at question time is usually better, cheaper, and easier to keep current.
Before fine-tuning, most needs are met by a good prompt with clear instructions and a few examples, or by retrieval for knowledge. These need no training run and are trivial to change. Reach for fine-tuning when prompting has genuinely hit its ceiling and you need consistency at scale.
A pretrained model is a talented new hire. A prompt is the instructions you give them for one task. Fine-tuning is putting them through a training course so a whole class of tasks becomes second nature. Retrieval is handing them the reference manual to look things up.
Berges AI shapes behavior without fine-tuning. Its interceptors adjust each response at use time, cutting filler or structuring reasoning, so you get a tuned feel with none of the cost, delay, or staleness of retraining a model.
Try Berges AIWhen you need consistent behavior at scale and a well-crafted prompt no longer gets you there. For most tasks, and especially for adding knowledge, prompting or retrieval is enough and much simpler.
It can, but poorly. The facts get baked in, are hard to update, and can still be recalled unreliably. For knowledge, retrieval that supplies the facts at question time is the better approach.
Not necessarily. Some closed providers offer managed fine-tuning through their API. Open-weight models give you the most freedom to fine-tune however you like on your own infrastructure.