by Amit Shekhar · 24 August 2026
How does Prefix Tuning work?
Prefix Tuning, a cheap way to adapt a large language model to a new task without changing the model itself. It saves memory, saves money, and lets one big model serve many different tasks at once. We will also see why full fine-tuning is so expensive, how the prefix is added and trained without changing the model, how Prefix Tuning differs from full fine-tuning and prompt tuning and when to use which one based on our use case, where it falls short and how it compares with LoRA, and where it is used in the real world.
Before you read, guessWhat process involves taking a trained model and continuing training on a specific task to update its parameters?
Ten seconds, a guess, then read — a wrong guess still makes the answer stick.
What this article covers
- What is a large language model?
- The problem: why full fine-tuning is expensive
- What is Prefix Tuning?
- Prefix Tuning = Prefix + Tuning
- How does Prefix Tuning work?
- The prefix is not real words
- Where the prefix is added
- How the prefix is trained
- How small is the prefix really?
- A simple code example
- Prefix Tuning vs Full Fine-Tuning
- Prefix Tuning vs Prompt Tuning
- Advantages of Prefix Tuning
- Limitations of Prefix Tuning
- Where Prefix Tuning is used
The article lives on outcomeschool.com. Read it there, then come back: the tutor in the margin has read it and will answer questions, and the questions below check what stayed.