What is Fine-tuning?

Fine-tuning is the process of taking a pre-trained AI model and further training it on specific data to improve performance for particular tasks, domains, or use cases. It allows customization without training a model from scratch.

When to consider Fine-tuning?

Fine-tuning is valuable when you have domain-specific data, need consistent output formatting, want to improve performance on specific tasks, or when general-purpose models don't meet your quality requirements.

When might Fine-tuning not be necessary?

Fine-tuning may not be needed when general-purpose models perform well, when you lack sufficient training data, or when prompt engineering can achieve desired results more cost-effectively.

What is the importance of Fine-tuning in AI/UX?

Fine-tuning enables more accurate, relevant, and consistent AI outputs for specific use cases, leading to better user experiences and more reliable AI-powered features in products.