Fine-tuning
ELI5 — The Vibe Check
Fine-tuning is like taking a smart graduate student who knows everything and then sending them to a specialist bootcamp. You start with a pre-trained model that already understands language, then train it further on YOUR specific data so it becomes an expert in your domain. It costs less than training from scratch and works way better.
Real Talk
Fine-tuning involves continuing the training of a pre-trained model on a task-specific or domain-specific dataset to adapt its behavior. The model's existing weights serve as the initialization, requiring far less data and compute than training from scratch. Techniques include full fine-tuning, LoRA, and RLHF.
When You'll Hear This
"We fine-tuned the model on our support tickets." / "Fine-tuning improved response quality dramatically."
Related Terms
LLM (Large Language Model)
An LLM is a humongous AI that read basically the entire internet and learned to predict what words come next, really really well.
Model
A model is the trained AI — the finished product.
Pre-training
Pre-training is the first massive phase where an AI reads basically the entire internet and learns to predict the next word billions of times.
Training
Training is the long, expensive process where an AI learns from data.
Transfer Learning
Transfer Learning is using knowledge a model already has from one task to help it with a different task.
Weights
Weights are the numbers inside a neural network that determine what it knows and how it behaves — they're the AI's 'brain cells.