OpenAI Official Guide: 6 Golden Rules for Unlocking the Potential of Reasoning Models (o1/o3)

With the release of OpenAI’s new generation of models capable of powerful reasoning, such as o1 and o3-mini, the rules of Prompt Engineering are being rewritten. The techniques we took for granted with GPT-4o—such as “Let’s think step by step” (Chain of Thought)—are now not only redundant but can even be counterproductive with these new models.

OpenAI has recently released a best practices guide specifically for reasoning models, summarizing 6 Golden Rules. This guide is more than just an instruction manual; it is a deep decoding of the new generation AI’s thought patterns. This article combines technical principles with real-world examples to help you collaborate efficiently with these “thinking” models.

Key Takeaways

• Keep it Simple: Reasoning models don’t need complex guidance; simple and clear instructions work best.
• No CoT: Do not use instructions like “think step by step,” as this interferes with the model’s native reasoning chain.
• Structured Separation: Use triple backticks, XML tags, etc., to clearly distinguish between instructions, data, and context.
• Limit RAG: In Retrieval-Augmented Generation, provide only the most relevant context to avoid information overload causing reasoning distractions.
• Explicit Constraints: Clearly define the boundaries and format of the output rather than vague quality requirements.
• Few-Shot Prompting: When dealing with complex formats, providing 1-2 examples (Few-Shot) is more effective than lengthy descriptions.

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1. Paradigm Shift: Why Do Reasoning Models Need a New Prompt Strategy?

Before diving into the 6 rules, we need to understand the fundamental difference between o1/o3 and traditional large models (like GPT-4o).

Traditional models are probabilistic prediction machines. They generate answers by predicting the next most likely token. To enable them to handle complex logic, we invented the “Chain of Thought” (CoT) technique, forcing the model to “write out” intermediate steps before outputting the result, thereby improving accuracy.

The o1/o3 series models, however, are native reasoning engines. Before generating an answer, they perform implicit, invisible internal reasoning (Internal Reasoning). This process is similar to a human making a draft in their mind before speaking.

What does this mean? When you tell a model that is already “thinking hard” to “think step by step,” it’s like teaching a math professor how to do addition—it’s not only unnecessary but can also interrupt their train of thought, causing them to output redundant content to cater to your instructions, or even lowering the quality of reasoning.

2. OpenAI’s Recommended 6 Golden Rules

To help developers adapt to this change, OpenAI has summarized the following 6 core recommendations:

2.1 Rule 1: Keep it Simple and Direct

Unlike GPT-4, o1 doesn’t need you to use polite phrases or complex role-playing to “coax” it. It has extremely strong instruction-following capabilities.

• ❌ Wrong Example (Over-guided):

  “You are a world-class Python expert, proficient in various algorithms. Please take a deep breath, think carefully, and help me write a Fibonacci sequence function…”

• ✅ Correct Example (Simple and Direct):

  “Write a Python script to calculate the Fibonacci sequence.”

Deep Dive: The attention mechanism of reasoning models is precious. Lengthy prompts dilute the model’s focus on the core task. Direct instructions allow the model to allocate more computing power to “solving the problem” rather than “understanding your pleasantries.”

2.2 Rule 2: Avoid Chain-of-Thought (CoT) Prompts

This is the biggest difference from traditional Prompt Engineering. Do not use the following instructions:

• “Let’s think step by step”
• “Explain your reasoning”
• “Show your work”

• ✅ Correct Practice: Ask the question directly. The model will automatically perform internal reasoning and then give you the final high-quality answer directly. If you really need it to show the process, explicitly ask to “include reasoning steps in the answer,” but do not try to guide how it reasons.

2.3 Rule 3: Use Delimiters

When a prompt contains multiple parts (such as instructions, data, reference documents), clear structure is crucial. This helps the model accurately identify which part is the object it needs to process.

• ✅ Recommended Delimiters:

  • Markdown delimiters: ---, ***
  • Triple quotes: """, '''
  • XML tags: <context>, <instruction>, <data>
  • Headers: # Instruction, # Context

• Real-world Case:

  # Instruction
  Analyze the sentiment of the following user review.
  
  # Data
  """
  This product is a disaster! Slow shipping, poor quality, and customer service ignores people.
  """

2.4 Rule 4: Limit Context in RAG

In RAG (Retrieval-Augmented Generation) scenarios, we are used to stuffing the model with large document chunks, hoping it will find the answer. But for reasoning models, too much irrelevant information is poison.

Reasoning models attempt to understand and integrate every piece of information you provide. If you provide 10 documents and 8 are irrelevant, the model will waste a lot of computing power analyzing those 8 irrelevant pieces, trying to find their connection to the problem (even if there is none).

• ✅ Best Practice: Before sending documents to o1, use a lighter model (like gpt-4o-mini) or traditional retrieval algorithms to perform a strict filter, keeping only the most relevant 1-3 document chunks.

2.5 Rule 5: Define Specific Constraints

Instead of telling the model to “write well,” define what “well” means. Reasoning models are very sensitive to specific boundary conditions.

• ❌ Vague Instruction:

  “Write a high-quality SQL query.”

• ✅ Specific Constraints:

  “Write a SQL query that:

  1. Uses CTE (Common Table Expressions) structure.
  2. Field names must use snake_case naming convention.
  3. Includes comments for complex logic.
  4. Is optimized for large dataset queries.”

2.6 Rule 6: Few-Shot Examples

When you need a specific output format or complex logic processing, Showing is more effective than Telling. Providing 1-2 perfect input-output pairs (Few-Shot) can instantly “align” the model with your needs.

• Real-world Case (Entity Extraction):

  Task: Extract medical entities from text.

  Example 1: Input: Patient complains of headache with mild fever (37.8°C). Output: {“symptom”: [“headache”, “fever”], “vital_sign”: {“temperature”: “37.8”}}

  Input: Abdominal pain relieved after taking ibuprofen. Output:

This is much more accurate than writing a thousand words saying “Please extract symptoms and vital signs in JSON format…“.

3. Practical Comparison: GPT-4o vs. o1-preview

To visually demonstrate these differences, let’s look at a programming task comparison.

Task: Refactor a piece of complex legacy code.

Prompt for GPT-4o (Old Paradigm):

You are a senior architect. Please read the following code. First, analyze the potential bugs and performance bottlenecks in the code step by step. Then, explain your refactoring ideas. Finally, give the refactored code. Please ensure the readability of the code.

Prompt for o1-preview (New Paradigm):

<code_snippet> [Paste Code] </code_snippet>

Refactor the above code to improve performance and maintainability. Constraints:

1. Use Python 3.10+ type hints.
2. Split large functions into single-responsibility small functions.
3. Output pure code, no explanation needed.

Result Analysis:

• GPT-4o needs you to guide it to “analyze first, then refactor,” otherwise it might directly give a piece of code that is not thoroughly modified.
• o1 receiving a simple instruction will automatically perform deep code flow analysis and boundary condition testing internally, and then directly output a nearly perfect refactored code. If you ask it to “explain ideas,” it might distract it, and the quality of the output code might actually decrease.

Conclusion and Outlook

OpenAI’s guide is not just about how to write prompts; it reveals an important trend in AI development: Models are becoming more like “brains” rather than “mouths.”

As reasoning capabilities become internalized, what we as developers and users need to do is no longer teach AI how to think, but tell it more precisely what to think about.

• Subtract: Remove redundant guidance and Chain of Thought instructions.
• Add: Add clear structure, explicit constraints, and high-quality examples.

Mastering these 6 rules will enable you to take the lead in harnessing powerful reasoning engines like o1 and o3, achieving unprecedented efficiency gains in fields such as code generation, complex data analysis, and scientific research.

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Disclaimer: This article is written based on OpenAI’s 2026 Reasoning Model Best Practices Guide. As model versions iterate (such as subsequent updates to o3-mini), specific performance may vary. Please refer to the latest official documentation.