What is Fine-tuning?
It is the process of specifically training a ready-made artificial intelligence model for a specific task or area of expertise.
Overview
Fine-tuning is the retraining of a general-purpose trained artificial intelligence with a small data set to perform a particular style, area of expertise or task better. This process preserves the general capabilities of the model while specializing it in a specific subject.
How it works
First, a basic model is selected and a data set specific to this model is displayed. The model undergoes light training on this data and adjusts its internal connections according to its new target.
Where it is used
It is used in very specific areas, such as legal copywriting, medical diagnostic support, or mimicking your company's private correspondence language.
Commonly confused with
It is frequently confused with RAG; RAG brings in information from outside, while fine-tuning permanently changes the behavior or expertise of the model.
Frequently asked questions
Is fine-tuning too expensive?
It has become much more accessible than before, but it still requires serious processing power and a quality data set.
Should I fine-tune the model every day?
No, fine-tuning is a permanent process; It makes much more sense to use RAG for up-to-date information.
Related terms
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This explanation was written in plain language for TreScout and machine-translated from the Turkish original · the Turkish version prevails. If something looks wrong or missing, write to hello@trescout.com. Read in Turkish →