What is Fine-Tuning?

Foundation AI models are trained on massive general datasets, producing broadly capable but non-specialized outputs. Fine-tuning starts from a pre-trained model and continues training it on a curated dataset representing the target domain. For video generation, fine-tuning can train a model to consistently generate a specific character, visual style, brand aesthetic, or product. For TTS, fine-tuning on voice samples creates a clone of a specific person's voice. For language models, fine-tuning on a creator's past scripts improves stylistic consistency. Fine-tuning requires fewer samples and less compute than training from scratch, making it accessible to individual creators and small teams. The result is a model that generates outputs specialized to the training domain while retaining general capability. In AI video workflows, fine-tuned models enable brand consistency and character continuity across multiple videos.