---
date: "2023-06-21T22:38:59.000Z"
title: "2023-06-21"
tags: ["language_models"]
draft: false
---

I've been thinking about the concept of "prompt overfitting".
In this context, there is a distinction between model overfitting and prompt overfitting.
Say you want to use a large language model as a classifier.
You may give it several example inputs and the expected outputs.
I don't have hard data to go by, but it feels meaningful to keep the prompt generic or abstract where possible rather than enumerating overly specific cases in a way that obfuscates the broader pattern you're hoping to apply.
I hypothesize these overly specific examples could interfere with the model output in unintended, overly restrictive ways.