What peak image prompt engineering looks like:

Who Should Read

Developers or creators who utilize image generation AI (such as Midjourney, DALL-E, Stable Diffusion, etc.) and want to improve their prompt writing skills.

Core Mechanics

• The original page was blocked by network security, so the actual post content could not be retrieved. Based on the title, it can be inferred that the post shares a case demonstrating 'the pinnacle of image prompt engineering'.
• Judging from the post title 'What peak image prompt engineering looks like', it is highly likely that it contains sophisticated prompt writing techniques or actual examples for generating high-quality images.
• The post was uploaded to the ChatGPT subreddit, so it is likely to be a prompt example related to ChatGPT's DALL-E image generation function.

Evidence

• "(No comment information)"

How to Apply

• You can check the actual image and prompt content by directly logging in to the original URL (https://www.reddit.com/r/ChatGPT/comments/1s8zocq/what_peak_image_prompt_engineering_looks_like/) with a Reddit account.
• If you are interested in image prompt engineering, you can find similar high-quality cases by searching for the keyword 'prompt engineering' in the r/ChatGPT, r/midjourney, and r/StableDiffusion subreddits.

Code Example

# 바로 써볼 수 있는 프롬프트 템플릿

## 기본형
Create an image of a random scene taken with an iPhone 6 with the flash on, chaotic, and uncanny.

## 장면 커스텀 버전
Create an image of [원하는 장면] taken with an iPhone 6 with the flash on, candid, chaotic.

## 변형 예시
- 'photo taken with a disposable camera, overexposed, chaotic street scene'
- 'security camera footage of an empty office at 3am, grainy, uncanny'
- 'Nokia phone camera photo of a birthday party, blurry, flash on, found photo'

# DALL-E API 예시 (Python)
import openai

client = openai.OpenAI()
response = client.images.generate(
    model="dall-e-3",
    prompt="A random scene taken with an iPhone 6 with the flash on, chaotic and uncanny",
    size="1024x1024",
    quality="standard",
    n=1,
)
print(response.data[0].url)

Terminology

Related Papers

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• ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning

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• Less Back-and-Forth: A Comparative Study of Structured Prompting

  체크리스트 형식으로 프롬프트를 구조화하면 LLM 답변 품질도 높아지고 토큰도 적게 쓴다.

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Related Resources

• Original Reddit Post