AI Glossary
Fine-Tuning
Understanding AI Terminology
Additional training of an AI model on specific data to customize its behavior.
What It Means
Fine-tuning is the process of further training a pre-trained AI model on a specific dataset or for a particular task. This customization helps the model better understand domain-specific language, follow specific formatting guidelines, or adopt particular styles. Fine-tuning is more efficient than training from scratch and allows creating specialized AI assistants.
Examples
- Fine-tuning on legal documents creates a law-focused assistant
- Customer support fine-tuning teaches brand voice and policies
- Code-focused fine-tuning improves programming capabilities
How This Applies to ARKA-AI
ARKA-AI supports various fine-tuned models specialized for different tasks, with ARKAbrain selecting the most appropriate model for each request.
Frequently Asked Questions
Common questions about Fine-Tuning
Most users don't need fine-tuning. Modern base models like GPT-4o and Claude are highly capable with good prompts. Fine-tuning is mainly for specialized business needs with consistent, specific requirements.
Prompting guides the model at inference time, while fine-tuning permanently changes the model's weights. Fine-tuning is more expensive but can produce more consistent results for specific use cases.
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