What is Fine-tuning in AI? Explained in Everyday Language for AI Beginners
Updates: AI for Absolute Beginners is an ongoing series that now covers:
- RAG
- Embedding
- Vector Database
- Fine-tuning (current)
You’ve likely encountered the term “fine-tuning” quite often in discussions about current AI applications. This term frequently appears alongside topics such as RAG, prompt design, and debates on whether fine-tuning is necessary. So in this article, let’s talk about what, why and when we need it.
What is fine-tuning and why it is important?
First of all, fine-tuning is not exclusive in the nowadays AI. It is a key concept in deep learning. In technical terms, fine-tuning involves taking a deep learning model that has been pre-trained on a large dataset and further training it on a smaller, specialized dataset.
So in simple words, it is a process of pushing an AI model from general to more specialized.
Fine-tuning in AI is like teaching an experienced chef a new recipe. Imagine a chef who’s mastered the art of Italian cuisine but wants to learn Japanese dishes. Instead of starting from scratch, the chef builds on their existing knowledge of flavors, techniques, and kitchen tools. This process is similar to fine-tuning in AI.
