Fine-tuning in Nebius AI Studio allows you to train models, so they can outperform generic models and fit domain-specific tasks better. Due to training on a large amount of data, fine-tuned models return outputs of higher accuracy, and the possibility of AI hallucinations lowers. Fine-tuning provides a way to train a model on a dataset. It serves as an alternative to prompting because fine-tuning does not suffer from a limited number of input examples that you can use in a prompt. As fine-tuning supports a larger number of examples, it eliminates the need for further extensive prompt engineering. As a result, fine-tuning lowers operational costs and reduces request latency. For more information about how to fine-tune a model, see the following: