---
title: Fine-Tuning
description: Page on Vedang Vatsa's site: https://veda.ng/glossary/fine-tuning
canonical: https://veda.ng/glossary/fine-tuning
last_updated: 2026-09-23
type: text/markdown
---
# Fine-Tuning

Source: https://veda.ng/glossary/fine-tuning
Author: Vedang Vatsa (https://veda.ng/about)

Fine-tuning is the process of taking a pre-trained model and continuing its training on a specialized dataset. Training a model from scratch requires massive computational resources and time. Fine-tuning reuses the foundational knowledge already learned. You start with a general model like GPT-3 and train it further on domain-specific data: medical texts, legal documents, code repositories, whatever you need.

The model adapts its behavior to match the patterns in your specialized dataset. A model fine-tuned on medical literature develops medical domain knowledge. One fine-tuned on code becomes better at programming tasks. Success requires dataset quality and size. Fine-tune on poor data and the model learns poor patterns. Fine-tune on too little data and overfitting happens: the model memorizes rather than learns. Fine-tune on too much data and the original knowledge gets overwritten.

Fine-tuning is how organizations create custom models for specific use cases. The organization that controls the specialized dataset can create a competitive advantage through fine-tuning. OpenAI documents fine-tuning as extra training on your examples so the model follows a style or task more reliably than prompting alone.

Glossary index: https://veda.ng/glossary