Quick answer: Adapt foundation models to a specific domain or task via supervised fine-tuning + LoRA.
Model Fine-Tuning is the process of taking a pre-trained foundation model (like Llama, Mistral, or GPT-3.5) and adapting it to perform exceptionally well on a specific task or domain. Instead of training from scratch—which requires massive computational resources—you leverage the model's existing knowledge and adjust its weights using your own labeled data. Combined with LoRA (Low-Rank Adaptation), a parameter-efficient technique, you can fine-tune models with just a fraction of the original training cost.
This skill lets you build domain-specific AI systems: a customer support chatbot trained on your company's documentation, medical diagnosis assistants fine-tuned on clinical datasets, or legal document analyzers trained on case law. Fine-tuning transforms generic models into specialized tools that understand your business context, terminology, and nuances.