What is fine tuning in AI

What is fine tuning in AI refers to understanding fine-tuning as a machine learning technique that adapts pre-trained models to specific tasks, domains, or datasets by continuing the training process on task-specific data while leveraging the general knowledge already encoded in the foundation model. This approach enables organizations to customize powerful pre-trained models for specialized applications without requiring the computational resources and time needed to train models from scratch, making sophisticated AI capabilities accessible for domain-specific use cases. Fine-tuning involves adjusting model weights through additional training on curated datasets that represent the target domain, task, or organizational requirements, allowing models to maintain their general capabilities while developing specialized expertise in specific areas. The process encompasses various techniques including full fine-tuning where all parameters are updated, parameter-efficient methods like LoRA and adapters that modify only small subsets of parameters, and instruction tuning that teaches models to follow specific command patterns and formatting requirements. Enterprise applications leverage fine-tuning for customizing language models for industry-specific vocabulary, adapting computer vision models for specialized image recognition tasks, and personalizing AI assistants for organizational workflows and communication styles. Advanced fine-tuning implementations support multi-task learning, domain adaptation, safety alignment, and performance optimization that enable organizations to create tailored AI solutions that balance general capability with specialized expertise for competitive advantage.