Learn Prompting

Prompt Engineering Guide

😃 Basics

🟢 Basics Guide Overview

🟢 What is Generative AI?

🟢 ChatGPT Basics

🟢 Testing Prompts with Interactive Learn Prompting Embeds

🟢 Introduction to Prompt Engineering

🟢 Basic Prompt Structure and Key Parts

🟢 Technique #1: Instructions in Prompts

🟢 Technique #2: Roles in Prompts

🟢 Technique #3: Examples in Prompts: From Zero-Shot to Few-Shot

🟢 Combining Prompting Techniques

🟢 Tips for Writing Better Prompts

🟢 Prompt Priming: Setting Context for AI

🟢 Differences Between Chatbots and LLMs

🟢 LLM Limitations: When Models and Chatbots Make Mistakes

🟢 What Can Generative AI Create Beyond Text?

🟢 How to Solve Problems Using Generative AI: A Simple Method

🟢 Next Steps: Where to Go From Here

💼 Applications

🟢 Introduction

🟢 Text Summarization

🟢 Table Generation

🟢 Multiple Choice Questions

🟢 Short-Form Content

🟢 Writing in Different Styles

🟢 Finding Emojis

🟢 Writing Emails

🟢 Blog Writing

🟢 Legal Documents

🟢 Study Buddy

🟦 Digital Marketing

🟦 Coding Assistance

🟦 Knowledge Base Chatbot

🟦 How to Build a Chatbot Using LLMs

🟦 Zapier for Emails

🧙‍♂️ Intermediate

🟢 Introduction

🟢 Chain-of-Thought Prompting

🟢 Zero-Shot Chain-of-Thought

🟦 Self-Consistency

🟦 Generated Knowledge

🟦 Least-to-Most Prompting

🟦 Dealing With Long Form Content

🟦 Revisiting Roles

🟦 More About Prompt Elements

🟦 Basic LLM Settings

🟦 OpenAI Playground

🧠 Advanced

🟢 Introduction

Zero-Shot

🟢 Introduction

🟢 Emotion Prompting

🟢 Role Prompting

🟢 Re-reading (RE2)

🟢 Rephrase and Respond (RaR)

🟦 SimToM

◆ System 2 Attention (S2A)

Few-Shot

🟢 Introduction

🟢 Self-Ask

🟢 Self Generated In-Context Learning (SG-ICL)

🟢 Chain-of-Dictionary (CoD)

🟢 Cue-CoT

🟦 Chain of Knowledge (CoK)

◆ K-Nearest Neighbor (KNN)

◆◆ Vote-K

◆◆ Prompt Mining

Thought Generation

🟢 Introduction

🟢 Chain of Draft (CoD)

🟦 Contrastive Chain-of-Thought

🟦 Automatic Chain of Thought (Auto-CoT)

🟦 Tabular Chain-of-Thought (Tab-CoT)

🟦 Memory-of-Thought (MoT)

🟦 Active Prompting

🟦 Analogical Prompting

🟦 Complexity-Based Prompting

🟦 Step-Back Prompting

🟦 Thread of Thought (ThoT)

Ensembling

🟢 Introduction

🟢 Universal Self-Consistency

🟦 Mixture of Reasoning Experts (MoRE)

🟦 Max Mutual Information (MMI) Method

🟦 Prompt Paraphrasing

🟦 DiVeRSe (Diverse Verifier on Reasoning Step)

🟦 Universal Self-Adaptive Prompting (USP)

🟦 Consistency-based Self-adaptive Prompting (COSP)

🟦 Multi-Chain Reasoning (MCR)

Self-Criticism

🟢 Introduction

🟢 Self-Calibration

🟢 Chain of Density (CoD)

🟢 Chain-of-Verification (CoVe)

🟦 Self-Refine

🟦 Cumulative Reasoning

🟦 Reversing Chain-of-Thought (RCoT)

◆ Self-Verification

Decomposition

🟢 Introduction

🟢 Chain-of-Logic

🟦 Decomposed Prompting

🟦 Plan-and-Solve Prompting

🟦 Program of Thoughts

🟦 Tree of Thoughts

🟦 Chain of Code (CoC)

🟦 Duty-Distinct Chain-of-Thought (DDCoT)

◆ Faithful Chain-of-Thought

◆ Recursion of Thought

◆ Skeleton-of-Thought

⚖️ Reliability

🟢 Introduction

🟢 Prompt Debiasing

🟦 Prompt Ensembling

🟦 LLM Self-Evaluation

🟦 Calibrating LLMs

🔓 Prompt Hacking

🟢 Introduction

🟢 Prompt Injection

🟢 Prompt Leaking

🟢 Jailbreaking

🟢 Defensive Measures

🟢 Introduction

🟢 Filtering

🟢 Instruction Defense

🟢 Post-Prompting

🟢 Random Sequence Enclosure

🟢 Sandwich Defense

🟢 XML Tagging

🟢 Separate LLM Evaluation

🟢 Other Approaches

🟢 Offensive Measures

🟢 Introduction

🟢 Simple Instruction Attack

🟢 Context Ignoring Attack

🟢 Compound Instruction Attack

🟢 Special Case Attack

🟢 Few-Shot Attack

🟢 Refusal Suppression

🟢 Context Switching Attack

🟢 Obfuscation/Token Smuggling

🟢 Task Deflection Attack

🟢 Payload Splitting

🟢 Defined Dictionary Attack

🟢 Indirect Injection

🟢 Recursive Injection

🟢 Code Injection

🟢 Virtualization

🟢 Pretending

🟢 Alignment Hacking

🟢 Authorized User

🟢 DAN (Do Anything Now)

🟢 Bad Chain

🖼️ Image Prompting

🟢 Introduction

🟢 Style Modifiers

🟢 Quality Boosters

🟢 Repetition

🟢 Weighted Terms

🟢 Fix Deformed Generations

🟢 Midjourney

🌱 New Techniques

🟢 Introduction

🟢 Aligned Chain-of-Thought (AlignedCoT)

🟦 Self-Harmonized Chain-of-Thought (ECHO)

🟦 Logic-of-Thought (LoT)

🟦 Narrative-of-Thought (NoT)

🟦 Code Prompting

◆ End-to-End DAG-Path (EEDP) Prompting

◆ Instance-adaptive Zero-Shot Chain-of-Thought Prompting (IAP)

🔧 Models

🟢 Introduction

🟢 Stable Diffusion 3.5

🟢 Anthropic Claude

🟦 Apple Intelligence Models

🟢 Google Gemini 1.5

🟢 Gemini 1.5 Flash

🟢 Gemini 1.5 Pro

🗂️ RAG

🟢 Introduction

🟦 Retrieval-Augmented Generation (RAG)

🟦 FLARE / Active RAG

🟦 Corrective RAG

🟦 Speculative RAG

🟦 Reliability-Aware RAG (RA-RAG)

🟦 Multi-Fusion Retrieval Augmented Generation (MoRAG)

🤖 Agents

🟢 Introduction

🟦 LLMs Using Tools

🟦 LLMs that Reason and Act

🟦 Code as Reasoning

💪 Prompt Tuning

🟢 Introduction

🟦 Prompt Tuning with Soft Prompts

🟦 Interpretable Soft Prompts

🟦 Prefix-Tuning

🟦 Prompt-Tuning with Perturbation-Based Regularizer

🟦 Low-Rank Prompt Tuning (LoPT)

🟦 Dynamic Prompting

🟦 Gradient-Free Prompt Tuning

🟦 Multitask Prompt Tuning

🔁 Language Model Inversion

🟢 Introduction

🟢 logit2prompt

🟢 output2prompt

🟢 Reverse Prompt Engineering (RPE)

🔨 Tooling

🟢 Introduction

Prompt Engineering Tools

Prompt Engineering IDEs

🟢 Introduction

GPT-3 Playground

Dust

Soaked

Everyprompt

Prompt IDE

PromptTools

PromptSource

PromptChainer

Prompts.ai

Snorkel 🚧

Human Loop

Spellbook 🚧

Kolla Prompt 🚧

Lang Chain

OpenPrompt

OpenAI DALLE IDE

Dream Studio

Patience

Promptmetheus

PromptSandbox.io

The Forge AI

AnySolve

Conclusion

📙 Vocabulary Resource

🎲 Miscellaneous

🟢 Introduction

🟢 Detection Trickery

🟢 Music Generation

🟢 Detecting AI Generated Text

📚 Bibliography

📦 Prompted Products

🛸 Additional Resources

🔥 Hot Topics

💪 Prompt Tuning🟢 Introduction

Introduction

🟢 This article is rated easy

Reading Time: 1 minute

Last updated on March 3, 2025

Sander Schulhoff

Takeaways

Have a basic understanding of what Prompt Tuning is

Prompt tuning is a technique for adapting pre-trained language models to downstream tasks without updating the model's core parameters. Instead of fine‑tuning all the weights, prompt tuning learns a small set of tunable parameters, called soft prompts, that are prepended or appended to the input. These soft prompts (continuous embeddings) are optimized via gradient descent so that, when combined with the (frozen) pre-trained model, they guide it to produce task-specific outputs.

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.

🟦 Interpretable Soft Prompts

🟦 Dynamic Prompting

🟦 Gradient-Free Prompt Tuning

🟦 Low-Rank Prompt Tuning (LoPT)

🟦 Multitask Prompt Tuning

🟦 Prefix-Tuning

🟦 Prompt-Tuning with Perturbation-Based Regularizer

🟦 Prompt Tuning with Soft Prompts

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.

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