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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

🖼️ Image Prompting🟢 Repetition

🟢 Repetition

Last updated on August 7, 2024 by Sander Schulhoff

What is Repetition?

Repetition is repeating the same word or similar phrases within a prompt. This technique can cause the model to emphasize that word in the generated image.

An Example of Repetition in Image Prompting

@Phillip Isola generated these waterfalls with DALLE:

A beautiful painting of a mountain next to a waterfall..

A very very very very very very very very very very very very very very very very very very very very very very beautiful painting of a mountain next to a waterfall.

The emphasis on the word very seems to improve generation quality! Repetition can also be used to emphasize subject terms. For example, if you want to generate an image of a planet with aliens, using the prompt A planet with aliens aliens aliens aliens aliens aliens aliens aliens aliens aliens aliens aliens will make it more likely that aliens are in the resultant image. The following images are made with Stable Diffusion.

A planet with aliens

A planet with aliens aliens aliens aliens aliens aliens aliens aliens aliens aliens aliens aliens

Conclusion

Repetition of a word or similar phrase can be used to emphasize the most important aspects of an image prompt and is therefore a useful technique in generating AI images.

FAQ

Why is repetition useful in image prompting?

As demonstrated in this article, repetition of a certain word (or similar phrases) can increase the likelihood of the generated output adhering to the instructions in the prompt.

Notes

This method is not perfect, and using weights (next article) is often a better option.

Footnotes

Oppenlaender, J. (2022). A Taxonomy of Prompt Modifiers for Text-To-Image Generation. ↩

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