Fine-Tuning
Fine-tuning is the process of further training a pre-trained AI model on domain-specific data to specialize its behavior and improve performance for particular tasks.
In Depth
While large language models are powerful general-purpose tools, fine-tuning adapts them to specific use cases. In customer support, fine-tuning a model on your company's historical tickets, product documentation, and brand guidelines creates an AI agent that speaks in your brand voice, understands your product terminology, and follows your support processes. The fine-tuning process involves curating high-quality training examples (typically hundreds to thousands of ideal conversations), running additional training cycles on the base model, and validating that the resulting model performs better on your specific tasks without losing general capabilities.
Fine-tuned models typically show higher accuracy, more consistent tone, fewer hallucinations about company-specific information, and better adherence to business rules compared to base models with prompting alone.
Related Terms
Large Language Model
A large language model (LLM) is a deep learning model trained on vast amounts of text data that can understand, generate, and reason about human language with remarkable fluency.
Model Training
Model training is the process of teaching an AI system to recognize patterns, make predictions, or generate outputs by exposing it to labeled or unlabeled data and adjusting its parameters.
Machine Learning
Machine learning is a branch of AI where systems learn patterns from data and improve their performance over time without being explicitly programmed for each task.
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