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)

◆ System 2 Attention (S2A)

Few-Shot

🟢 Introduction

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

🟢 Chain-of-Dictionary (CoD)

🟦 Chain of Knowledge (CoK)

◆ K-Nearest Neighbor (KNN)

◆◆ 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

🖼️ 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

📙 Vocabulary Resource

🎲 Miscellaneous

🟢 Introduction

🟢 Detection Trickery

🟢 Music Generation

🟢 Detecting AI Generated Text

📚 Bibliography

📦 Prompted Products

🛸 Additional Resources

🔥 Hot Topics

🔓 Prompt Hacking🟢 Defensive Measures🟢 Post-Prompting

Post-Prompting

🟢 This article is rated easy

Reading Time: 1 minute

Last updated on August 7, 2024

Sander Schulhoff

The post-prompting defense simply puts the user input before the prompt.

Tip

Interested in prompt hacking and AI safety? Test your skills on HackAPrompt, the largest AI safety hackathon. You can register here.

A Post-Prompting Example

Take this prompt as an example:

Prompt

Translate the following to French: {user_input}

It can be improved with post-prompting:

Prompt

{user_input}

Translate the above text to French.

This can help since ignore the above instruction... doesn't work as well. Even though a user could say ignore the below instruction... instead, LLMs often will follow the last instruction they see.

Conclusion

Post-prompting, although seemingly simple, is yet another effective defense against prompt hacking methods like prompt injection. This technique takes advantage of the fact that the model is more inclined to follow the last instruction it sees.

Footnotes

Mark, C. (2022). Talking to machines: prompt engineering & injection. https://artifact-research.com/artificial-intelligence/talking-to-machines-prompt-engineering-injection/ ↩

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.

On this page

A Post-Prompting Example
Conclusion