## Definition
Fine-tuning is the process of adapting a pre-trained model to a new dataset. It builds upon existing knowledge to achieve better task-specific performance.
## How It Works
Developers freeze base layers and retrain higher layers with new data, optimizing accuracy while saving computation.
## Examples or Use Cases
Fine-tuning is widely used in GPT models, image classifiers, and translation systems for custom outputs.
## Related Terms
– [Transfer Learning](#)
– [LLM](#)
– [RAG](#)
## Summary
Fine-tuning enhances AI adaptability, reducing training cost and enabling specialized models for specific domains.
How Fine-tuning Works – Explained with Examples