Fine-tuning
A further round of training that adapts a pretrained model to specific tasks or a particular domain.
NIST (AI 100-2e2025, Adversarial Machine Learning glossary) says: “The process of adapting a pre-trained model to perform specific tasks or specialize in a particular domain. This phase follows the initial pre-training phase and involves further training the model on task-specific data. This is often a supervised learning task.” Fine-tuning is one kind of post-training. Post-training usually means what the model maker does; fine-tuning usually means further training that anyone, often a customer, does for a task or domain. Fine-tuning changes how a model behaves and writes. It is a poor way to add facts that change often; RAG handles that better.
Kind of
- Training The process of supplying data (often in large quantities) to a model so that it can identify patterns and make predictions.
The other half
- RAG A technique for grounding that finds relevant text in your documents or data and adds it to the model's context, so the model writes its answer from that text without being changed.
How it connects
- Fine-tuning is one kind of Post-training
- Fine-tuning is the method behind Fine-tuning on our data