Technique Updated 2026-04
Fine-tuning
Definition
Fine-tuning is the process of retraining an existing AI model on a specific dataset to adapt it to a particular domain or task.
See also in the glossary
L
LLM (Large Language Model)
An LLM is an AI model trained on billions of texts, capable of understanding and generating human language.
R
RAG (Retrieval-Augmented Generation)
RAG is a technique that connects an LLM to external data sources to generate more accurate and up-to-date answers.
M
Machine Learning
Machine Learning is a branch of AI where systems learn from data to improve their performance without being explicitly programmed for each task.
L
LoRA
LoRA is an efficient fine-tuning technique that adapts an AI model by only adjusting a fraction of its parameters, drastically reducing cost.
Tools that use fine-tuning
Frequently Asked Questions
What's the difference between fine-tuning and RAG?
Fine-tuning modifies the model by retraining it. RAG provides information to the model at query time without modifying it. RAG is simpler and more flexible, fine-tuning gives more integrated results.
Is fine-tuning expensive?
It depends. Full LLM fine-tuning costs thousands to millions of dollars. But techniques like LoRA allow fine-tuning at lower cost by only adjusting a fraction of parameters.
