Fine-tuning

Fine-tuning takes a pre-trained model and continues training it on examples specific to your use case, baking a behavior or format into the weights. It’s well suited to consistent tone, structured output, or a narrow skill.

It’s less suited to injecting changing knowledge — for that, retrieval (RAG) is cheaper to update and easier to keep accurate. Updating fine-tuned knowledge means retraining.

The strongest systems often combine the two: retrieval for facts, light fine-tuning for behavior and format.