Optimizing Prompts for OpenAI Reasoning Models

Introduction

OpenAI's reasoning models are designed to process and generate responses efficiently, but crafting the right prompt is crucial for optimal performance. Many conventional prompt engineering techniques, such as "think step by step," may not enhance results and could even be counterproductive. This guide outlines the best strategies for constructing effective prompts.

1. Developer Messages Over System Messages

As of o1-2024-12-17, OpenAI's reasoning models now prioritize developer messages instead of system messages. This change aligns with the model’s chain of command behavior, ensuring that prompts are processed with greater consistency.

2. Keep Prompts Simple and Direct

Reasoning models excel with straightforward, concise instructions. Avoid overcomplicating your prompts with excessive context or unnecessary phrasing. Instead, focus on clarity to improve response accuracy.

Example:

Good Prompt: "Summarize the latest trends in AI ethics."

Less Effective Prompt: "Please analyze and break down step by step the latest debates around AI ethics, including major academic opinions and industry perspectives."

3. Avoid Chain-of-Thought Prompts

Unlike some previous AI models, OpenAI's reasoning models do not require chain-of-thought prompting (e.g., "think step by step"). They handle internal reasoning effectively without such guidance. Adding this type of instruction may actually hinder performance rather than improve it.

4. Use Delimiters for Clarity

Using delimiters such as markdown, XML tags, or section titles helps separate different parts of your input, ensuring the model interprets them correctly.

Example:

### Introduction
Explain the impact of AI on modern healthcare.

### Key Developments
List three recent breakthroughs in AI-assisted diagnostics.        

5. Limit Additional Context in RAG

When using retrieval-augmented generation (RAG), only provide the most relevant information. Too much context can overwhelm the model and lead to unnecessary complexity in responses.

Best Practice:

• Include only the most pertinent facts or excerpts.
• Avoid feeding in entire documents when a few key points suffice.

6. Try Zero-Shot First, Then Few-Shot if Needed

OpenAI’s reasoning models often generate high-quality responses without needing examples (zero-shot learning). Only if results are inconsistent should you introduce few-shot examples, ensuring they closely match the instructions.

Example:

Zero-shot prompt: "Generate a product description for a smart home security camera."

Few-shot prompt (if needed):

Example Input: "Write a description for a high-end smartwatch."
Example Output: "The QuantumX Smartwatch offers advanced fitness tracking, seamless connectivity, and a sleek design."

Now, generate a product description for a smart home security camera.        

7. Provide Specific Guidelines

To obtain precise responses, clearly define the constraints of your request.

Example:

Effective Prompt: "Suggest three marketing strategies for a startup with a budget under $1,000."

Vague Prompt: "What are some marketing strategies for a new business?"

8. Define Success Criteria

State clear success parameters to guide the model toward a satisfactory response.

Example:

"List three actionable growth strategies for a SaaS company. Each strategy must:

1. Be implementable within six months.
2. Cost under $5,000.
3. Have at least one real-world example."

This approach ensures responses remain relevant and actionable.

9. Signaling Markdown Formatting in API Calls

With the o1-2024-12-17 update, reasoning models will not use markdown formatting in API responses by default. If you require markdown, include the phrase:

Formatting re-enabled        

on the first line of your developer message to instruct the model accordingly.

Conclusion

By following these best practices, you can enhance the performance of OpenAI's reasoning models, ensuring clarity, efficiency, and high-quality output. Keep prompts concise, avoid unnecessary reasoning instructions, and define clear success criteria to achieve the best results.