Prompt Engineering Guide
πŸ˜ƒ Basics
πŸ’Ό Applications
πŸ§™β€β™‚οΈ Intermediate
🧠 Advanced
Special Topics
🌱 New Techniques
πŸ€– Agents
βš–οΈ Reliability
πŸ–ΌοΈ Image Prompting
πŸ”“ Prompt Hacking
πŸ”¨ Tooling
πŸ’ͺ Prompt Tuning
πŸ—‚οΈ RAG
🎲 Miscellaneous
Models
πŸ“ Language Models
Resources
πŸ“™ Vocabulary Resource
πŸ“š Bibliography
πŸ“¦ Prompted Products
πŸ›Έ Additional Resources
πŸ”₯ Hot Topics
✨ Credits
πŸ’ͺ Prompt Tuning🟒 Introduction

Introduction

🟒 This article is rated easy
Reading Time: 1 minute
Last updated on August 7, 2024

Sander Schulhoff

Takeaways
  • Have a basic understanding of what Prompt Tuning is

In the following section, we will be talking about soft prompts and interpretable soft prompts. This is a step up in difficulty as it involves LLM concepts that we have not covered prior. Soft prompts involve a concept called Prompt Tuning, which freezes the LLM's weights and instead modifies the input. It is important to recognize that this is a concept discussed previously and offers a novel approach to leveraging LLMs.

By the end of this section, you will have an understanding of how Prompt Tuning works and how soft prompts can be used to enhance the performance and interpretability of your GenAI applications.

Sander Schulhoff

Sander Schulhoff is the Founder of Learn Prompting and an ML Researcher at the University of Maryland. He created the first open-source Prompt Engineering guide, reaching 3M+ people and teaching them to use tools like ChatGPT. Sander also led a team behind Prompt Report, the most comprehensive study of prompting ever done, co-authored with researchers from the University of Maryland, OpenAI, Microsoft, Google, Princeton, Stanford, and other leading institutions. This 76-page survey analyzed 1,500+ academic papers and covered 200+ prompting techniques.

Interpretable Soft Prompts

Soft Prompts