Fine-tuning is the process of adapting a pre-trained machine learning model—often a large language model or other foundation model—to a specific task, dataset, or domain by continuing its training on new data. This approach allows organizations to build on the model’s existing general capabilities while tailoring it to their unique requirements, such as industry-specific terminology, tone, compliance standards, or customer interactions.
Fine-tuning is especially useful in enterprise settings where generic out-of-the-box AI may not deliver the accuracy, relevance, or alignment needed. It can significantly improve performance in tasks like document classification, chatbot conversations, summarization, or sentiment analysis. However, fine-tuning requires high-quality labeled data, infrastructure, and oversight to avoid issues such as overfitting or introducing bias. It differs from prompt engineering, which modifies model behavior without altering the underlying model weights.