Fine-tuning takes a pre-trained large language model — which has learned general knowledge from vast internet-scale data — and continues training it on a smaller, domain-specific dataset to specialize its behavior. This allows organizations to create AI models that perform better on their specific tasks, adopt a particular tone or style, or work within industry-specific constraints. Fine-tuned models are more efficient and accurate for targeted applications than general-purpose models used with prompts alone.