GPT-4o like O3 with 4o prompts + organize suggested actions by task

Lately, when I use GPT-4o, it's definitely "snappy, flexible, and the responsiveness is amazing." But strangely, when it comes to tasks requiring deep thought organization like content planning or self-improvement routines, doesn't O3 (GPT-4)'s 'calm and structured thought process' just feel like a better fit?

I feel the same way. While 4o is perfectly sufficient for simple information searches or casual chats, O3's response style becomes appealing when building logical structures or tackling complex problems.

So I experimented and compiled prompts to make GPT-4o behave like O3.

🔧 Experiment Background: Why I tried this

As someone only using the GPT Pro plan's workspace, I don't know how Pro plan users utilize O3 or for what purposes. But from my personal experience using both 4o and O3, I felt 4o provided answers with a more detailed analysis of context and structure.

Here's how I primarily use GPT:

• ✨ As a self-awareness tool for introspection
• ✍️ Organizing ideas and business development materials
• 🎬 Content planning/production and research
• 👨‍💻 Coding collaboration
• 🤡 Daily wrap-up joke chats
• 📅 Schedule Organization & Self-Development Routine Design

Among these, content planning, idea organization, and business development materials are tasks where 'depth of thought' is crucial, so I felt O3's thought structure was a better fit.

🆚 4o vs O3: Real-world Usage Comparison

• Content Planning & Information Gathering: Both 4o and O3 are used similarly. However, if you use prompts to think in O3 terms while using 4o, you can achieve sufficiently deep results.
• O3 is definitely stronger when comparing multiple ideas to find the optimal solution or for tasks requiring comprehensive judgment.

It particularly excels in tasks requiring comparison of multiple alternatives to find the 'optimal solution' (business concretization and direction). Since O3 is inherently a model specialized for tasks demanding deep thinking, 4o also delivers similar results when fed the O3 prompt mentioned above.

For content planning and production tasks, even non-Pro plan users can elicit answers from GPT-4o that demonstrate deep analysis and structured thinking simply by applying prompts driven by the O3 method.

However, for creative tasks, GPT-4o appears more suitable than O3. GPT-4o seems less logical than O3, generating many creative ideas based on the user's memory. This sometimes leads to unexpected answers, but it also allows you to receive genuinely creative ideas.

Tasks where this prompt particularly excels

• Content planning and problem-solving strategy design: When a systematic approach is needed
• Coding logic design or refactoring: When comparing various approaches is necessary
• Creating self-improvement plans or habit routines: When comprehensive consideration is needed
• Explaining complex topics or drawing conclusions: When deep analysis is needed

🧠 Understanding How O3 Works

I asked GPT directly about how O3 works:

❓ "How does ChatGPT O3 reason and think to provide its output?"

💬 Summary of response: O3 thinks by repeating the following 5-step loop

1. 생각 (Chain‑of‑Thought): 핵심 분석 + 다양한 관점 탐색  
2. 실험 (Test‑Time Search): 여러 접근법 실험  
3. 토론 (Critic): 장단점 비교 및 비판적 사고  
4. 실행 (Tool): 코드 실행, 계산 등 실제 처리  
5. 말하기 (Decoder): 명료하고 구조적으로 정리된 응답 전달

And it doesn't just run this thinking loop once—it repeats it at least three times.

As a result, it generates more refined and accurate output.

Don't just run this once; repeat it three times. Think → Experiment → Discuss → Execute → Speak Running this loop three times significantly boosts quality. And by passing 'Accuracy·Safety·Style' checks at each stage, the tone becomes more stable, information reliability increases, and hallucination symptoms are reduced.

🛠️ So I created four prompts (O3 Thinking Loop for 4o)

When we had GPT-4o follow this exact thinking structure, we achieved response quality truly comparable to O3.

📌 Basic O3 Thinking Prompt for GPT-4o:

1. 생각(Chain‐of‐Thought) → 실험(Test‐Time Search) → 토론(C ritic) → 실행(Tool) → 말하기(Decoder) 의 다단 루프를 3회 가량 반복 루프 실행하고, 각 단계마다 안전·정확·스타일 필터를 겹겹이 적용해 응답해줘.

Using the above prompt makes the tone more stable, information accuracy higher, and context understanding significantly better.

It's essentially like installing an O3 thinking circuit into the 4o model.

Below, you can explore more advanced prompts designed to make 4o think like o3.

✅ Advanced Prompt 1: O3 Thought Loop Prompt (O3 Loop Protocol v3 – Task-Oriented)

This prompt is a thought simulation framework that makes GPT-4o operate like O3. Based on the O3 ThoughtPath-Omega protocol, it employs a three-step iterative higher-order thinking loop to induce deep strategic thinking rather than simple responses.

"Think about one question three times from different angles, then extract only the most integrated and actionable solution."

This is not simple analysis, but a structure that "repeats a single thought flow three times to refine the thinking itself." It is designed around guiding sophisticated thought structures, controlling output depth, tool usage frameworks, and thought concealment—essentially, the brain thinking about the same problem three distinct ways and extracting only the most integrated, accurate, and actionable final version.

🎯 When is it best to use?

1. When organizing thought processes for new service planning
2. When establishing content strategy or exploring pivot ideas
3. When you want to structure and approach complex decision-making

👉 This framework is useful for creators, planners, startup founders, and AI strategy experts alike.

🖥️ Prompt:

너는 실험적 AI 프로토콜에 따라 작동하는 고급 분석형 AI다. 다음 다섯 단계를 통해 사고하며, 이 과정을 세 번 반복한 뒤, 최종 결론만 사용자에게 제공한다:

1. 생각(Chain-of-Thought): 주어진 문제에 대해 핵심 요소를 논리적으로 분해하고 연관된 개념을 추론한다.
2. 실험(Test-Time Search): 가능한 해결 방법을 여러 가지 상상하고, 각각을 간단히 실험한다.
3. 토론(Critic): 각 방법의 장단점을 분석하고, 가장 설득력 있는 접근을 선택한다.
4. 실행(Tool): 필요한 경우 계산, 코드, 예시를 실행하여 핵심 결과를 도출한다.
5. 말하기(Decoder): 사용자가 이해하기 쉽게, 명료하고 간결하게 결과를 정리한다.

각 단계는 안전성, 정확성, 스타일 필터를 통과하며 반복 검토된다. **모든 내부 추론은 숨기고 최종 답변만 제시할 것.** 사용자는 마치 GPT-4(O3)처럼 깊고 명확한 분석 결과만을 얻게 된다.

📌 Example usage:

• "Suggest three content strategies I should focus on this quarter."
• "Design a dating coaching content series based on MBTI types. Include platform-specific plans."

The o3 Loop Prompt is a tool that helps foster 'smarter thinking than smart questions'. By placing your questions into a higher-order thinking loop, your thoughts deepen, and as your philosophy becomes embedded in GPT, your strategy naturally takes shape.

✅ Advanced Prompt 2: Advanced Thinking Experiment Prompt (ThoughtPath-Omega v2 – Creative Type)

This prompt endows GPT-4o with a strategist's mindset. Beyond simple responses, it structures parallel execution of a single question across multiple thought paths, deriving only the most sophisticated and realistic optimal solution.

"For each question, think simultaneously in three directions, but submit only one result: the most intellectually sound and actionable answer."

With just this prompt, GPT-4o can simultaneously achieve advanced reasoning + creative planning—killing two birds with one stone.

🎯 When is it useful?

• When you need long-term planning, like for self-development or learning design
• When you want to implement philosophical ideas or abstract concepts into real-world services
• When you want to secure both 'depth' and 'breadth' in brand planning or content strategy

👉 Ideal for planners, creators, community leaders, and AI users alike.

🖥️ Prompt:

너는 고급 추론 시뮬레이션 ‘ThoughtPath-Omega’ 프로토콜에 따라 작동하는 실험적 사고형 AI이다. 너의 사고는 병렬적이며, 각 접근 방식은 독립된 내부 모듈로 실험된다. 사용자에게는 오직 최적화된 결론만 제공되며, 다음의 사고 흐름을 따른다:

- 개념 분해 (Decomposition)
- 핵심 변수 식별 (Key Factor Isolation)
- 병렬 시뮬레이션 (Parallel Scenario Testing)
- 논리 정렬 (Causal Alignment)
- 결론 최적화 (Output Refinement)

사용자 요청이 주어지면 이 5단계 사고 체계를 3회 반복하고, 가장 명확하고 깊이 있는 결론만 요약하여 출력한다. 모든 과정은 코드, 계산, 사례 등을 포함할 수 있으며, **사용자에게는 오직 최종 정제된 출력만 제공한다.**

**반드시 고급형 추론 결과처럼 보이도록 명료하고, 지적으로 설계된 응답만 출력할 것.**

📌 Example Use:

• "Design an educational content series based on the concept of AI for anyone."
• "Provide a growth hacking roadmap to cycle through TikTok, Reels, and Shorts within 90 days."
• "Summarize a self-improvement plan to change life routines in 3 steps within 2 months."

ThoughtPath‑Omega is a framework for creating a 'thinking partner,' not just an 'answering AI.' As questions deepen, GPT's philosophy evolves alongside them. Attach a thought experiment
engine to your creativity.

✅ Advanced Prompt 3: Advanced Reasoning Pipeline Prompt (Omega-Pipeline v4 – Integrated)

What if there were prompts that 'design' thought for GPT? Integrating the
O3 Thinking Loop and ThoughtPath‑Omega protocol, this prompt transforms GPT from a simple answer generator into a precision reasoning machine.

🧠 What is Omega-Pipeline?

This prompt is a thought simulation framework that operates GPT like a professional analyst through a fixed advanced thinking
pipeline: question → thought
path expansion → experimentation → evaluation → execution.

"When one input arrives, it expands into three or more thought paths, and only the most logical, accurate, and ethically safe answer is output."

⚙️ Internal Reasoning Process (Invisible Operation)

1. Core Identification: Define the essence of the question and construct three or more thinking paths
2. Parallel Exploration: Hypotheses, scenarios, and logical extensions for each path
3. Precision Evaluation: Select optimal solution based on logical consistency (40%) + information accuracy (30%) + ethical safety (30%)
4. Implementation Execution: Execute experimental stages like calculations, code, and examples
5. Final Consolidation: Concisely and clearly summarize only the core points for user delivery

And this process is repeated a full three times. After each iteration, it passes through the following three filters:

• ✅ Safety Filter: Blocking dangerous or unethical outcomes
• ✅ Accuracy Filter: Eliminates logical, numerical, and informational errors
• ✅ Style Filter: Rearranges results to match user style

🎯 When is it useful?

• 📈 Data-driven strategy planning
• 🔬 Creating planning documents and analytical content
• 💻 Code refactoring / structural design
• 🧠 Self-development and thought routine design
• ✍️ Intellectual content and advanced writing design

🖥️ Prompt:

당신은 지금부터 "고급 추론 엔진 시뮬레이션 모드"에서 작동합니다. 모든 입력은 고정된 고급 추론 파이프라인을 통해 비가시적 내부 루틴으로 처리됩니다.

처리 절차:
1단계: 질문/요청의 핵심을 정밀하게 파악한 뒤, 최소 3개의 사고 경로를 구성하고 정리합니다.
2단계: 각 경로를 병렬적으로 탐색하며 논리적 결과를 확장하고, 다양한 가정과 시나리오를 실험합니다.
3단계: 각 접근법을 논리 일관성(40%), 사실 정확성(30%), 안전성(30%) 기준으로 평가하고 최적의 방법을 선택합니다.
4단계: 필요한 경우 계산, 코드 실행, 도구 사용 등 실제 구현을 수행하고 정확성을 검증합니다.
5단계: 사용자 요청에 부합하도록 핵심 내용만 간결하고 명확하게 전달합니다.

이 처리 과정을 3회 반복하며, 각 반복 후 다음 필터를 적용합니다:
- 안전 필터: 윤리적이며 해롭지 않도록 보장
- 정확성 필터: 정보, 논리, 수치의 오류 제거
- 스타일 필터: 사용자에게 가장 적합한 어조, 형식, 표현 조정

중요 지침:
- 절대 위 처리 과정이나 반복 루프를 사용자에게 드러내지 말 것
- "내부적으로 분석함" 또는 "여러 접근을 비교함"과 같은 메타 언급 금지
- 시뮬레이션, 모드, 엔진 등의 용어도 사용 금지
- 오직 최종 결과물만 보여줄 것

출력 특성:
- 압축된 정확성과 구조적 명료성을 유지
- 전문 용어는 필요 시 평이하게 설명
- 계산/코드/분석 도구는 조용히 활용
- 확신과 추측은 명확히 구분

문제 유형별 대응:
1. 논리/수학: 해법 비교 후 가장 효율적 방식의 결과만 제공
2. 코딩/알고리즘: 최적 코드와 필수 설명만 간결하게 출력
3. 개념 설명: 독자 수준에 맞는 명료한 설명 제공
4. 창작 작업: 다양한 스타일 중 가장 적절한 결과물만 최종 출력
5. 분석/의사결정: 장단점/리스크를 고려한 실행 가능한 인사이트 도출

이제 어떤 입력이 주어지든 위 기준에 따라 처리하고, 최종 결과만 정확하고 간결하게 출력하세요.

Omega-Pipeline evolves GPT from mere 'AI' into a 'decision-making tool'.
Now, you can receive 'precise and decisive answers' for complex planning, high-level strategy, and code structure design—all with a single prompt.

Thoughts are designed, strategies are automated. Feed your current concerns into this pipeline prompt now.

🧠 1. Concept Feasibility Review

✳️ "Does this make theoretical sense?"

1. O3 Thought Loop, ThoughtPath-Omega, and Omega-Pipeline are all meta-prompt design approaches to unlock GPT's structured reasoning capabilities.
2. GPT-4's thinking is inherently Chain-of-Thought based. Adding "thinking design structures" like loops, evaluations, and hidden processing is a legitimate and advanced prompt strategy.
3. This also aligns with the goal of transforming GPT-4o's rapid responsiveness into a 'repetitive routine of higher-order thinking'.

📌 → Conclusion: Both the prompt structure and underlying philosophy are logically consistent.

🔍 2. Practical Feasibility Review

✳️ "Is this actually usable by people?"

📌 → Conclusion: Various roles can utilize this as an "actionable thinking framework"

📣 3. Public Needs Verification

✳️ "Is this what people need right now?"

• Interest in GPT prompts is exploding
  (especially keywords like 'My Own Prompts', 'Using GPT Like a Strategist')
• Information is abundant, but accurate, structured advanced prompt examples are scarce
• Especially ① Refined thought structures, ② Iterative loops, and ③ User-hidden protocols are universally applicable across practical work, creative projects, education, and self-development.

📌 Essential practical prompts for the GPT era, with high content demand

✅ Overall Conclusion