Fine-Tuning

Fine-tuning is the targeted retraining of a pre-trained AI model with your own example data so it handles a particular task or style better. It changes the model itself, in contrast to RAG, which supplies the model with current knowledge at runtime without retraining it.

What is fine-tuning?

Fine-tuning builds on an already trained Large Language Model. You train it further with additional examples of your own so it hits a special task or a fixed style more reliably. Unlike training from scratch, fine-tuning needs comparatively little data, but it changes the model itself permanently.

Fine-tuning, RAG, or prompt?

Businesses have three ways to make an AI fit, and they solve different problems:

• Prompt engineering tells the AI by instruction how it should behave. Fast and without training.
• RAG makes current factual knowledge from your documents available to the AI without changing the model. Ideal against outdated answers.
• Fine-tuning shapes behaviour and style deeply, for example a very specific professional jargon. More involved and impractical when facts change, because every update means retraining.

Is fine-tuning worth it for SMBs?

For most small and mid-sized businesses, the answer is: rarely. When the point is for the AI to know current prices, opening hours, or product details, RAG is the right and far cheaper route. Fine-tuning only pays off when a very distinct tone or a highly specialised task type can't be captured any other way.

SendSeven's approach

SendSeven is a unified messaging platform with a built-in AI assistant. Instead of fine-tuning a model for each customer, SendSeven relies on RAG: the assistant answers based on your knowledge base and stays current the moment you change a document. You set the tone via a prompt. That way you get a fitting AI with no training effort. The knowledge base guide shows how to store your content for the AI assistant. In line with GDPR, Made in Germany. Try it free for 14 days.