Fine-tuning is the process of taking a pre-trained AI model and training it further on your specific data to make it better at a particular task. For example, you might fine-tune a language model on your company's support tickets so it learns your terminology, product names, and common issues. The result is a model that performs better on your specific use case than the generic version. Fine-tuning is more expensive and complex than RAG (retrieval-augmented generation) but produces more natural, consistent outputs for specialized tasks. Most mid-market companies start with RAG and only fine-tune if RAG doesn't meet their quality bar.