Prompt Engineering for OpenAI’s O1 and O3-mini Reasoning Models

OpenAI’s O1 and O3-mini models are advanced systems designed for deep reasoning—meaning they “think” through problems much like a human would. Unlike the standard GPT-4 (sometimes called GPT-4o), these models are built to work through multiple steps internally without needing you to tell them to “think step by step.” Let’s break down how they differ from GPT-4o and discuss some best practices for designing prompts to get the best results.

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Key Differences Between O1/O3-mini and GPT-4o

1. Input Structure and Context Handling

• Built-In Reasoning:
  O1-series models come with an internal chain-of-thought. They naturally break down and analyze complex problems without extra nudges. GPT-4o, however, may need you to say things like “let’s think step by step” to work through multi-step problems.
• Background Information Needs:
  GPT-4o has a wide knowledge base and, in some cases, tools like browsing or plugins. In contrast, O1 and O3-mini have a more limited background on niche topics. This means if your task involves specific or less-common information, you need to include those details in your prompt.
• Context Length:
  O1 can handle up to 128,000 tokens and O3-mini up to 200,000 tokens (with up to 100,000 tokens in output). This is much more than GPT-4o, which allows you to include very detailed inputs—ideal for tasks like analyzing lengthy case files or large datasets.

2. Reasoning Capabilities and Logical Deduction

• Depth and Accuracy:
  O1 and O3-mini are optimized for deep, multi-step reasoning. For instance, in complex math problems, O1 performed significantly better than GPT-4o because it naturally works through each step internally.
  • Complex Tasks: They excel in problems that require many steps (5 or more), producing highly accurate results.
  • Simple Tasks: For very basic questions, their tendency to “overthink” can sometimes be a drawback compared to GPT-4o, which might give a quick, straightforward answer.
• Self-Checking:
  O1 models internally verify their answers as they work, which often leads to fewer mistakes when handling tricky or multi-layered problems.

3. Response Characteristics and Speed

• Detail vs. Brevity:
  Because they reason deeply, O1 and O3-mini tend to give detailed, step-by-step answers. If you prefer a concise answer, you need to instruct the model to be brief.
• Performance Trade-offs:
  • Speed and Cost: O1 is slower and more expensive because of its detailed reasoning process.
  • O3-mini: Offers a good balance—it’s cheaper and faster while still strong in STEM tasks, though it might not be as strong in general knowledge as GPT-4o.

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Best Practices for Prompt Engineering with O1 and O3-mini

To make the most of these models, here are some actionable tips:

Keep Your Prompts Clear and Direct

• Be Concise:
  State your question or task clearly without extra words. For example, instead of writing a long explanation with lots of fluff, simply say:

  “Solve the following puzzle and explain your reasoning.”

• Minimal Context:
  Only include necessary details. Overloading the prompt with too much extra information or multiple examples can actually confuse the model.

Use Few or No Examples

• Zero-Shot is Often Best:
  Unlike earlier models that might need several examples to understand the task, O1 and O3-mini perform best with little to no examples. If you must include one, keep it extremely simple and relevant.

Set a Clear Role or Style with System Instructions

• Role Definition:
  You can start with a short instruction like:

  “You are a legal analyst explaining a case step by step.”
  This helps the model adopt the right tone and focus on the task.

• Specify Output Format:
  If you need your answer in a specific format (bullet points, a list, JSON, etc.), mention that in your prompt. For example:

  “Provide your answer as a list of key steps.”

Control the Level of Detail

• Directly Specify Verbosity:
  Tell the model exactly how detailed you want the answer to be. For a short answer, say:

  “Answer in one paragraph.”
  For a detailed breakdown, you could say:
  “Explain all the steps in detail.”

• Use Reasoning Effort Settings (for O3-mini):
  If your interface allows it, adjust the reasoning effort (low/medium/high) based on how complex your task is.

Ensure Accuracy in Complex Tasks

• Provide Clear Data:
  If your task includes numbers or specific facts (like in a legal case), structure them clearly. Use bullet points or tables if necessary.
• Ask for Self-Check When Needed:
  For critical tasks, you might ask the model to double-check its work. For example:

  “Analyze the data and verify that your conclusion is consistent with the facts.”

• Iterate When Necessary:
  If the answer isn’t quite right, try a slightly rephrased prompt. Running the prompt a few times and comparing results can increase confidence in the final answer.

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Example: Applying These Practices to a Legal Case Analysis

Imagine you need a legal analysis using one of these models. Here’s how you might structure your prompt:

1. Outline the Facts Clearly:
   Begin with a list of the key facts. For example:

   “- Party A and Party B entered a contract on 2025.

   • There was a disagreement about delivery dates.”
     Then ask:
     “Based on the above facts, determine if Party A is liable for breach of contract under U.S. law.”

2. Include Relevant Legal Context:
   If the analysis depends on specific laws or precedents, include that text in the prompt.

   “According to [Statute X]: [insert excerpt]. Apply this statute to the case.”

3. Set the Role and Format:
   Provide a system instruction such as:

   “You are a legal analyst. Use the IRAC format (Issue, Rule, Analysis, Conclusion) in your response.”

4. Control the Level of Detail:
   Specify if you want a thorough explanation or a brief summary:

   “Explain your reasoning in detail, covering each step of the legal analysis.”

5. Ask for Verification:
   Finally, add:

   “Double-check that all facts are addressed and that your conclusion logically follows.”

By following these steps, you guide the model to produce a well-structured and accurate legal analysis.

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Summary of Best Practices

• Be clear and concise: Focus on your main question and include only the necessary details.
• Limit examples: Use zero-shot or at most one simple example.
• Define roles and formats: Set the model’s persona and output style early on.
• Control verbosity: Directly instruct whether you want a brief or detailed response.
• Provide clear data: Structure any critical facts or data clearly.
• Verify critical outputs: Ask the model to double-check its reasoning for complex tasks.

Using these guidelines helps you tap into the powerful reasoning capabilities of O1 and O3-mini. They’re best for in-depth tasks like complex legal analysis, detailed problem solving in math, or other situations where a step-by-step breakdown is essential. For simpler queries, GPT-4o might be faster and more direct, so always choose the right tool for your task.

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This plain-language rewrite covers all the ins and outs of prompt engineering for OpenAI’s advanced reasoning models, ensuring you have actionable insights to optimize your prompts for accurate and detailed responses.