Fine-tuning is the process of taking a general AI model (e.g. GPT-4) and further training it on your own data so it performs better in a specific domain or follows your company's communication style.
Think of it this way: the base model can write emails in English. After fine-tuning on a hundred examples of your best sales emails, it will write in the same tone, with the same structure, and will know what tends to work in your context.
Fine-tuning makes sense when: you want a model that writes in a specific style; you need a specialised model for a narrow domain (legal texts, medical documentation); you want a smaller, faster, cheaper model for high-volume tasks.
Important distinction: fine-tuning is not the same as RAG. RAG gives the model access to documents at runtime – fine-tuning changes the model's underlying "reflexes".
Most companies need RAG, not fine-tuning. Fine-tuning makes sense at high volumes and when you have very specific style requirements.