Fine-Tuning

Fine-tuning is the process of taking a pre-trained AI model and training it further on your specific data to improve its performance on tasks relevant to your business. While a general model knows how language works, fine-tuning teaches it your specific terminology, use cases, and preferred response styles.

Fine-tuning vs. RAG

Fine-tuning and RAG serve different purposes. Fine-tuning improves the model’s base knowledge and behavior. RAG lets the model look up specific information from your data at query time. Many custom AI systems use both: fine-tuning for tone and behavior, RAG for factual accuracy.

When fine-tuning makes sense for small businesses

Fine-tuning is most valuable when your business uses specialized terminology, industry jargon, or specific response formats. For most small businesses, RAG alone provides sufficient accuracy without the complexity and cost of fine-tuning. BrandCurb recommends fine-tuning only when RAG is not enough.