The perfect prompt structure for OpenAI’s o1 model

Whether you’re using OpenAI’s models for content creation, code generation, data analysis, or simply to explore the boundaries of AI capabilities, understanding and implementing a robust prompt structure is paramount. In this blog post, we’ll delve into these four essential pillars, breaking down each component and illustrating how you can apply them to create prompts that consistently deliver exceptional and accurate AI responses. Get ready to elevate your prompt engineering skills and unlock the true potential of OpenAI’s advanced AI.

The Four Pillars of Perfect Prompts: A Framework for OpenAI Success

According to Greg Brockman, the key to crafting truly effective prompts for OpenAI models lies in a structured approach built upon four fundamental pillars. These pillars ensure clarity, context, and ultimately, better AI responses. Let’s examine each pillar in detail:

Pillar 1: Define Your Goal Clearly – What Do You Truly Want?

The first and arguably most crucial pillar is clearly defining your goal. Before you even begin typing your prompt, take a moment to precisely articulate what you want the AI to achieve. Vague or ambiguous prompts often lead to generic or unsatisfactory responses. Clarity in your objective is the foundation upon which a successful prompt is built.

Consider the difference between these two prompts:

• Vague Prompt: “Tell me about hiking near San Francisco.”
• Clear Goal Prompt: “I want a list of the best medium-length hikes within 2 hours of San Francisco. Each hike should provide a cool and unique adventure and be lesser known.”

The vague prompt leaves much to interpretation, potentially resulting in a broad overview of popular and well-known hikes. In contrast, the clear goal prompt specifies the desired type of hikes (medium-length, unique, lesser-known, within a specific radius), setting precise parameters for the AI. By clearly defining your goal, you guide the AI to focus on the specific information you need, significantly increasing the relevance and value of the response.

Actionable Tips for Defining Your Goal Clearly:

• Be Specific: Avoid general requests. The more specific you are about what you need, the better the AI can understand and fulfill your request.
• Identify Keywords: Pinpoint the core keywords related to your goal. This helps you focus your prompt and ensures the AI hones in on the relevant concepts. In the hiking example, keywords are “best,” “medium-length hikes,” “San Francisco,” “unique adventure,” “lesser known.”
• Outline Desired Outcome: Imagine the perfect response in your mind. What would it look like? What information would it contain? Describing the ideal output to yourself before writing the prompt greatly enhances clarity.

Pillar 2: Specify the Return Format – Structure for Success

The second pillar focuses on specifying the return format. Just as important as defining what you want is defining how you want the AI to present the information. Do you need a list, a paragraph, a table, JSON, code, or a specific style of writing? Clearly stating your desired output format ensures the response is not only informative but also readily usable and aligned with your needs.

Think about how you intend to use the AI’s response. If you need to easily extract data, a structured format like a list or a table is preferable. If you need to embed the output directly into a document, a paragraph or a specific writing style might be more suitable. By specifying the format, you are essentially instructing the AI on how to organize and present the information in the most useful way for you.

Consider these examples relating to our hiking prompt:

• Format-Agnostic Prompt (building upon Clear Goal): “Give me details about the best medium-length hikes within 2 hours of San Francisco that are unique and lesser known.”
• Format-Specific Prompt: “Provide a list of the top 3 best medium-length hikes within 2 hours of San Francisco that are unique and lesser known. For each hike, list the name (as found on AllTrails), starting and ending locations, distance, drive time from San Francisco, hike duration, and what makes it unique. Present each hike as a numbered list item.”

The format-specific prompt explicitly requests a numbered list and specifies the exact pieces of information to include for each hike. This level of detail ensures the AI generates a response that is not only informative but also structured for easy consumption and comparison.

Actionable Tips for Specifying the Return Format:

• Choose the Right Structure: Determine if a list, table, paragraph, code block, JSON, or other format best suits your needs.
• Define Specific Details: If you need a list or table, specify the columns or data points you require. If you need text, specify the desired tone, style, or length.
• Use Formatting Keywords: Utilize keywords in your prompt like “list,” “table,” “in JSON format,” “as code,” “in bullet points,” “numbered list,” “summarize in one paragraph,” etc., to guide the AI.

Pillar 3: Include Warnings & Constraints – Guide for Accuracy and Relevance

The third pillar involves including warnings and constraints. This is about proactively guiding the AI to be mindful of specific limitations, biases, or crucial details that could affect the quality and accuracy of the response. Warnings and constraints act as guardrails, preventing the AI from making assumptions or overlooking critical factors.

Imagine asking an AI to provide financial advice. Without warnings, the AI might generate advice without considering individual circumstances or risk tolerance. However, by adding warnings, you can steer the AI towards more responsible and contextually appropriate responses. Similarly, constraints help narrow down the scope and ensure relevance.

In our hiking example, while not strictly “warnings,” we can think of constraints like specifying “lesser known hikes.” This constraint guides the AI to filter out popular tourist trails and focus on more unique and potentially less crowded options.

Let’s enhance our hiking prompt with more explicit constraints and implicit “warnings”:

• Prompt with Constraints: “Provide a list of the top 3 best medium-length hikes within 2 hours of San Francisco that are unique and lesser known. Focus on hikes suitable for moderately experienced hikers who enjoy scenic views and a bit of solitude. Exclude extremely challenging or dangerous hikes. “

By adding constraints about hiker experience level, desired scenery, and avoidance of dangerous hikes, we are giving the AI crucial context to filter and prioritize hikes based on specific criteria. This implicitly acts as a “warning” to avoid suggesting hikes that might be unsuitable for the intended user.

Actionable Tips for Including Warnings & Constraints:

• Identify Potential Pitfalls: Think about potential biases, inaccuracies, or undesirable outputs the AI might generate.
• Set Boundaries: Use phrases to exclude certain types of responses or focus on specific criteria (e.g., “Do not include…”, “Focus on…”, “Exclude…”, “Only consider…”).
• Specify Safety or Ethical Considerations: If relevant, include warnings about safety, ethical implications, or responsible use of the information.
• Request Double-Checking: Explicitly ask the AI to double-check specific details or sources if accuracy is paramount.

Pillar 4: Provide Context (Context Dump) – Background for Better Understanding

The final pillar, often referred to as a “context dump,” involves providing background information to the AI. This is about giving the AI the necessary context to fully understand your request and generate a more relevant, nuanced, and personalized response. The more context you provide, the better the AI can tailor its output to your specific situation and needs.

Think of it like briefing a human expert. The more background information you give them about your situation, needs, and preferences, the better they can understand your request and provide tailored advice. The same principle applies to AI models. Context helps the AI go beyond simply processing keywords and truly understand the underlying intent and purpose of your prompt.

Let’s add a “context dump” to our hiking prompt, drawing from Greg Brockman’s example where he explains his hiking experience and what he’s looking for:

• Prompt with Context Dump: “Provide a list of the top 3 best medium-length hikes within 2 hours of San Francisco that are unique and lesser known. For each hike, list the name (as found on AllTrails), starting and ending locations, distance, drive time from San Francisco, hike duration, and what makes it unique. Focus on hikes suitable for moderately experienced hikers who enjoy scenic views and a bit of solitude. Exclude extremely challenging or dangerous hikes. Context: I am planning a weekend hiking trip near San Francisco. I have some hiking experience and I am looking for hikes that are not too crowded and offer a sense of adventure and discovery. I’m particularly interested in hikes with unique natural features or interesting historical aspects. Uniqueness is very important for this trip.“

The added context provides the AI with crucial background information: the user’s hiking experience, the purpose of the hike (weekend trip, adventure/discovery), and the importance of uniqueness. This context allows the AI to further refine its search and prioritize hikes that align with these specific preferences, resulting in a more personalized and relevant response.

Actionable Tips for Providing Context (Context Dump):

• Explain Your Situation: Briefly describe your background, needs, goals, or the reason behind your request.
• Share Relevant Background: Provide any information that might be pertinent to the AI’s understanding of your prompt. This could include your expertise level, prior experiences, or specific preferences.
• Clarify Intent and Purpose: Explain why you are asking this question and what you intend to do with the information.
• Use “Context:”, “Background:”, or “My situation is:” to clearly demarcate the context section in your prompt, making it easier for both you and the AI to identify it.

Putting It All Together: Greg Brockman’s Perfect Prompt Example

Greg Brockman effectively demonstrated this four-pillar framework with his hiking example, as highlighted throughout this blog post. Let’s recapitulate his example and explicitly break it down against the four pillars:

Brockman’s Example Prompt (Deconstructed):

1. Goal: “I want a list of the best medium length hikes within 2 hours of San Francisco. Each hike should to provide a cool and unique adventure and be lesser known.” (Pillar 1: Define Your Goal Clearly)
2. Return Format: “the hikes name as found on all Trails starting and ending locations distance drive time hike duration and what makes it unique plus he requests the top three hikes” (Pillar 2: Specify the Return Format) – He requests a list of the top three hikes and specifies the exact information points he wants for each hike.
3. Warnings/Constraints: (Implicitly in “lesser known” and “unique adventure”) While not explicit warnings, the request for “lesser known” and “unique adventure” functions as constraints, guiding the AI to avoid suggesting common, popular hikes and prioritize unique, less-traveled trails. We could further enhance this with explicit constraints about difficulty level or scenery preferences. (Pillar 3: Include Warnings & Constraints)
4. Context Dump: “he explains why he needs this information his hiking experience what he’s looking for and why uniqueness matters for this trip” (Pillar 4: Provide Context) – Brockman provides context about his purpose (a trip where uniqueness matters) and indirectly implies his desire for a more adventurous, less mainstream hiking experience.

By systematically applying these four pillars, Greg Brockman crafted a prompt that is highly effective in eliciting the desired information from OpenAI’s models, demonstrating the practical power of this structured approach.

Why This Framework Works: Benefits of Structured Prompts

Adopting the four-pillar prompt structure brings numerous benefits beyond simply getting “an” answer from the AI. It empowers you to get better answers, more consistently, and with greater efficiency.

• Improved Clarity and Accuracy: Clearly defined goals and formats minimize ambiguity and guide the AI toward generating more focused and accurate responses.
• Increased Relevance and Personalization: Context dumps enable the AI to tailor responses to your specific needs and preferences, making the output more relevant and valuable.
• Reduced Iteration and Refinement: Well-structured prompts often require less back-and-forth and fewer revisions, saving time and effort in achieving the desired result.
• Enhanced Control and Predictability: By specifying formats and constraints, you gain greater control over the AI’s output and increase the predictability of the response.
• Unlocking Advanced AI Capabilities: For complex tasks and advanced models like OpenAI’s ‘o1’, structured prompts are essential to leverage their full potential and achieve sophisticated outcomes.

Beyond the Basics: Advanced Prompting Tips

While the four pillars provide a solid foundation, here are some additional tips to further enhance your prompt engineering skills:

• Iterate and Experiment: Prompt engineering is often iterative. Don’t be afraid to experiment with different phrasing, formats, and levels of detail to refine your prompts and optimize results.
• Start Simple, Then Refine: Begin with a basic prompt and gradually add complexity and detail based on the initial responses.
• Use Examples (Few-Shot Prompting): For more complex tasks, providing a few examples of the desired input-output pairs can significantly improve the AI’s understanding and performance.
• Break Down Complex Tasks: For intricate problems, break them down into smaller, more manageable prompts.
• Stay Updated on Model Capabilities: AI models are constantly evolving. Stay informed about the latest features and best practices for prompting specific models.

Conclusion: Master the Art of Prompt Engineering for AI Success

In conclusion, mastering prompt engineering is becoming an increasingly vital skill in the age of powerful AI models like those from OpenAI. By embracing the four-pillar framework – Define Your Goal Clearly, Specify the Return Format, Include Warnings & Constraints, and Provide Context (Context Dump) – you can significantly enhance the effectiveness of your prompts and unlock the full potential of these advanced technologies.

Greg Brockman’s insights and the hiking example perfectly illustrate the practical application of this framework. Start implementing these pillars in your own interactions with OpenAI models, and you’ll undoubtedly witness a marked improvement in the quality, relevance, and utility of the AI responses you receive. Embrace structured prompting, experiment with different approaches, and unlock a new level of AI-powered productivity and creativity.