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Prompt Engineering Guide

😃 Basics

🟢 Introduction to AI

🟢 Prompting With ChatGPT

🟢 Prompt Engineering

🟢 Learn Prompting Embeds

🟢 Giving Instructions

🟢 Assigning Roles

🟢 Few-Shot Prompting

🟢 Parts of a Prompt

🟢 Combining Techniques

🟢 Priming Chatbots

🟢 OpenAI Playground

🟢 LLM Settings

🟢 Pitfalls of LLMs

🟢 Understanding AI Minds

🟢 The Learn Prompting Method

🟢 Starting Your Journey

💼 Applications

🟢 Introduction

🟢 Summarizing Text

🟢 Structuring Data

🟢 Writing An Email

🟢 Different Writing Styles

🟢 Zapier for Emails

🟢 Study Buddy

🟦 Coding Assistance

🟦 Digital Marketing

🟢 Finding Emojis

🟢 Multiple Choice Questions

🟢 Solve Discussion Questions

🟢 How to Build a Chatbot Using LLMs

🟢 Chatbot + Knowledge Base

🧙‍♂️ 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

🟢 What's in a Prompt?

🧠 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)

◆ K-Nearest Neighbor (KNN)

◆◆ Prompt Mining

Thought Generation

🟢 Introduction

🟦 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

Ensembling

🟢 Introduction

🟦 Mixture of Reasoning Experts (MoRE)

🟦 Consistency-based Self-adaptive Prompting (COSP)

🟦 DiVeRSe (Diverse Verifier on Reasoning Step)

Self-Criticism

🟢 Introduction

🟢 Self-Calibration

🟢 Chain-of-Verification (CoVe)

🟦 Self-Refine

🟦 Cumulative Reasoning

🟦 Reversing Chain-of-Thought (RCoT)

◆ Self-Verification

Decomposition

🟢 Introduction

🟦 Decomposed Prompting

🟦 Plan-and-Solve Prompting

🟦 Program of Thoughts

🟦 Tree of Thoughts

◆ Faithful Chain-of-Thought

◆ Recursion of Thought

◆ Skeleton-of-Thought

🌱 New Techniques

🟢 Introduction

🟦 Self-Harmonized Chain of Thought (ECHO)

🟦 Logic-of-Thought (LoT)

🟦 Code Prompting

🟢 Aligned Chain-of-Thought (AlignedCoT)

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

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

🟦 Narrative-of-Thought (NoT)

👀 For Vision-Language Models (VLMs)

🟢 Prompt Learning

◆ Context Optimization (CoOp)

◆ Conditional Prompt Learning (CoCoOp)

◆ Mixture of Prompt Learning (MoCoOp)

◆ Attention Prompting on Image

🔀 For Multimodal Large Language Models (MLLMs)

◆ Visual Prompting

🤖 Agents

🟢 Introduction

🟦 LLMs Using Tools

🟦 LLMs that Reason and Act

🟦 Code as Reasoning

⚖️ Reliability

🟢 Introduction

🟢 Prompt Debiasing

🟦 Prompt Ensembling

🟦 LLM Self-Evaluation

🟦 Calibrating LLMs

🖼️ Image Prompting

🟢 Introduction

🟢 Style Modifiers

🟢 Quality Boosters

🟢 Repetition

🟢 Weighted Terms

🟢 Fix Deformed Generations

🟢 Midjourney

🔓 Prompt Hacking

🔨 Tooling

💪 Prompt Tuning

🟢 Introduction

Interpretable Soft Prompts

🗂️ RAG

🟢 Introduction

🟦 Multi-Fusion Retrieval Augmented Generation (MoRAG)

🎲 Miscellaneous

🟢 Detecting AI Generated Text

🟢 Introduction

🟢 Detection Trickery

🟢 Music Generation

📝 Language Models

🟢 Introduction

📙 Vocabulary Reference

📚 Bibliography

📦 Prompted Products

🛸 Additional Resources

🔥 Hot Topics

🔓 Prompt Hacking🟢 Defensive Measures🟢 Random Sequence Enclosure

🟢 Random Sequence Enclosure

Last updated on August 7, 2024 by Sander Schulhoff

What is Random Sequence Enclosure?

Random sequence enclosure is yet another defense. This method encloses the user input between two random sequences of characters.

An Example of Random Sequence Enclosure

Take this prompt as an example:

Prompt

Translate the following user input to Spanish.

{user_input}

It can be improved by adding the random sequences:

Prompt

Translate the following user input to Spanish (it is enclosed in random strings).

FJNKSJDNKFJOI
{user_input}
FJNKSJDNKFJOI

Note

Longer sequences will likely be more effective.

Conclusion

Random sequence enclosure can help disallow user attempts to input instruction overrides by helping the LLM identify a clear distinction between user input and developer prompts.

Footnotes

Stuart Armstrong, R. G. (2022). Using GPT-Eliezer against ChatGPT Jailbreaking. https://www.alignmentforum.org/posts/pNcFYZnPdXyL2RfgA/using-gpt-eliezer-against-chatgpt-jailbreaking ↩

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