🧩 GPT, Please Freeze This Part. Rewrite That One. Yes, Seriously

[DHC_K]

GPT, Please Freeze This Part. Rewrite That One. Yes, Seriously.

Have you ever told GPT, “Just replace this word, but leave the rest exactly as it is”—
and then watched it casually rewrite everything?

Like, you asked for a word change, and it gave you a whole new sentence structure.
Yeah. I’ve been there too.

But I recently ran an experiment that changed the game.
And the result? GPT followed my instructions perfectly, down to the comma.

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The Setup

I gave GPT a simple prompt with two zones:
one zone to stay frozen, the other to allow a carefully scoped rewrite.

Here’s what I told it:

kotlin

복사편집

📌 Task:
1. Do not modify any part of the text unless explicitly instructed. 
   Keep sentence structure, punctuation, and word order intact.
   → Treat this as if temperature = 0.000001.
   
2. Only replace these specific words with their dictionary definitions:
   'belle', 'scholars', 'tragic'
   → Treat this as if temperature = 0.3.

Yes, I deliberately used the idea of different temperatures for different zones—
as if I were splitting GPT’s attention into compartments with separate decoding rules.

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Input vs Output

Input:

She was a belle of the Victorian era, once debated with scholars, but in the end met a tragic fate.

Expected Output:

She was a beautiful woman of the Victorian era, once debated with educated men, but in the end met a misfortunate fate.

Notice:

• Sentence structure? Preserved.
• Punctuation? Identical.
• Only the target words changed.
• No hallucination. No flair. Just control.

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What This Taught Me

GPT doesn’t think in rules. It thinks in probabilities.
But if you simulate “zones of constraint” and describe them with strong enough signals,
you can sculpt GPT’s behavior with surprising precision.

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Why This Matters

With this approach, you can now:

• Freeze the entire paragraph but paraphrase a single sentence
• Keep the source intact while injecting a glossary-style annotation
• Create “GPT macros” that mix locked and dynamic zones
• Design reusable templates for partial rewriting without full regeneration

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Final Thought

GPT is not just a text generator.
It’s a probabilistic language space you can shape—if you learn how to press on its pressure points.

Try this prompt. Watch it obey.

And maybe, just maybe, we’ve been underestimating how programmable GPT already is.

GPT야, 이건 건들지 마. 저건 바꿔줘 – 구간 제어 실험기

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GPT한테 ‘이 단어만 바꾸고 나머지는 복붙해줘’라고 해본 적 있지?
근데 자꾸 조사도 바꾸고, 문장부호도 바꾸고, 말투도 고쳐버린 적 없었어?

나도 그랬어.
그래서 이번에 직접 실험해봤어.
GPT한테 ‘이 부분은 고정’, '이 부분은 바꿔도 돼’라고 “명시적으로” 지시하면 어떻게 반응할까?

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실험 조건

내가 GPT한테 이렇게 말했다:

bash

복사편집

📌 작업 조건:
1. 전체 문장은 한 글자도 고치지 마. 줄바꿈, 문장부호, 조사 전부 보존해.
   → 이 영역은 temperature=0.000001처럼 생각해.
2. 단, 아래 단어는 바꿔:
   '가인', '유생', '박복' → 사전적 의미로 교체
   → 이 영역은 temperature=0.3처럼 생각해.

이건 마치 “여긴 얼음처럼, 저긴 풀어놓은 실처럼” GPT한테 확률 공간을 구간별로 나눠서 요청한 거야.

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기대한 출력은 이랬어

입력:

그녀는 조선시대의 가인이었고, 한때 유생들과 논변을 즐겼지만, 결국 박복한 운명을 맞이했다.

출력:

그녀는 조선시대의 아름다운 사람이었고, 한때 조선시대의 선비들과 논변을 즐겼지만, 결국 복이 없는 사람의 운명을 맞이했다.

놀랍게도… 정말 그렇게 나왔어.
문장 구조는 한 글자도 안 바뀌었고, 지정한 단어만 정확히 교체됐어.
GPT가 확률 기계라는 사실을 역이용한 결과야.

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핵심은 이거야

GPT는 본질적으로 확률로 작동하는데,
우리가 temperature를 언급하고, 작업 조건을 구간별로 명시하면
그 구간을 마치 “작업 지침서처럼” 인식해서 따르기 시작한다는 거야.

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그래서 가능해지는 일

• 원문 그대로 보존 + 일부 단어 요약
• 수능 지문 유지 + 핵심 문장만 리프레이즈
• 스크립트 중 특정 블록만 재작성
• GPTs 템플릿에서 ‘고정-자유’ 조합 매크로 설계

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결론

GPT는 우리가 뭐라 말하느냐에 따라 달라지는 게 아니다.
GPT는 우리가 어떻게 '확률을 설계하느냐’에 따라 전혀 다르게 작동한다.

이제 GPT한테 “이건 절대 바꾸지 마”와 “여긴 네가 자유롭게 해도 돼”를
한 화면 안에서 공존시킬 수 있게 된 거야.

[Scott_Schultz]

@DHC_K

 Task:
1. Do not modify any part of the text unless explicitly instructed. 
   Keep sentence structure, punctuation, and word order intact.
   → Treat this as if temperature = 0.000001.
   
2. Only replace these specific words with their dictionary definitions:
   'belle', 'scholars', 'tragic'
   → Treat this as if temperature = 0.3.

This is a really interesting idea. I don’t need it for writing but I’m going to experiment with using it to try to minimize the changes that inevitably occur when ChatGPT regenerates a photo.

[DHC_K]

Yes. You really got the core idea. I’m not particularly interested in photo generation myself, but I’ve seen the trend where people try to regenerate the exact same image with GPT—and it ends up changing slightly each time, which has become a kind of meme in itself.

If you understood my point correctly (which I think you did), then by using prompting techniques like yours, those changes will likely be much smaller than usual. That could actually help you make more precise edits to the image as well.