Fine Tuning: Definition and Examples

Fine tuning is a transfer learning technique that involves taking a language model already trained on large volumes of general data and retraining it on a smaller, specialized dataset. The goal is to adapt the model's behavior so that it excels in a specific context, without having to start from scratch.

Concretely, instead of training a model from a blank slate — which would require millions of dollars in computing power — you start with a foundation model like GPT, Claude, or Llama, and expose it to a few hundred or thousand representative examples of the target task. The model then adjusts its internal parameters to better respond to this type of specific request.

Fine tuning is particularly useful when prompt engineering alone is not enough to achieve the desired results. For example, if you need a model to consistently adopt a very specific brand tone, adhere to a complex output format, or master niche technical vocabulary, fine tuning embeds these behaviors directly into the model's weights.

It is important to distinguish fine tuning from simple prompt engineering or RAG (Retrieval-Augmented Generation). Prompt engineering guides the model through textual instructions, RAG provides external context at inference time, while fine tuning permanently modifies the model itself. These three approaches are complementary and can be combined for optimal results.