Hint Engineering for OpenAI O1 and O3-mini Inference Models

Introduction: OpenAI's O1 and O3-mini are advanced "reasoning" models that differ from the base GPT-4 (commonly known as GPT-4o) in the way they process prompts and generate answers. These models are designed to spend more time "thinking" about complex problems, mimicking human analytical methods.

This paper provides an in-depth look at OpenAI for O1 cap (a poem) O3-mini Prompt engineering techniques for reasoning models. However, the insights on input structure, reasoning capabilities, response characterization, and Prompt best practices set forth in the paper, the Not limited to OpenAI models . With the boom in inference modeling techniques, there has been an influx of such as DeepSeek-R1 and many other models with superior reasoning capabilities. The core principles and techniques in this paper can likewise serve as a valuable reference to help readers use the DeepSeek-R1 and other similar inference models when maximizing their potential. Therefore, after gaining insight into the O1 and O3-mini Prompt EngineeringThe details are accompanied by an invitation to readers to think about how these lessons can be blended and applied to the broader field of inference modeling to unlock more powerful AI application capabilities.

O1/O3-mini vs. GPT-4o

Input structure and contextualization

• Built-in reasoning vs. cue-guided reasoning: The O1 series models haveBuilt-in chain of thought reasoning skillsThis means that they reason internally without additional guidance from cue words. In contrast, GPT-4o usually needs external instructions such as "let's think step-by-step" to guide it when solving complex problems, because it doesn't automatically perform the same level of multi-step reasoning. With O1/O3-mini, you can just ask the question; the model will analyze it in depth on its own.
• The need for external information: GPT-4o has an extensive knowledge base and access to tools (e.g., browsing, plug-ins, visualization) in some deployments, which helps it deal with a variety of topics. In contrast, the O1 model is outside the training focus of theNarrower knowledge base. This means that when using the O1/O3-mini, if the task is beyond common sense, theImportant background information or context should be included in the prompts--Don't assume that the model knows niche facts. gpt-4o may already know some legal precedent or obscure detail, whereas O1 may need you to provide that text or data.Tip Example:
  • GPT-4o: "An analysis of the recent U.S. Supreme Court decision on abortion rights." (GPT-4o may already have knowledge)
  • O1: "Analyze the impact of the abortion rights ruling on U.S. society based on the following background information: [paste summaries of relevant news stories and legal documents]." (O1 may need more detailed background information)
• Context length: The inference model has a very large context window. o1 supports up to 128k token inputs and o3-mini accepts up to 200k tokens (up to 100k token outputs), exceeding the context length of GPT-4o. This allows you to feed a large number of case files or datasets directly into O1/O3.For prompting projects, clearly organize large inputs(using sections, bullets, or headings) so that the model can navigate the information.Both GPT-4o and O1 can handle long prompts, but the higher capacity of O1/O3 means that you can include more detailed context in a single input, which is very useful in complex analyses.Tip Example:
  • "Summarize the core points of contention in the case and the court's final decision based on this lengthy legal document pasted below. [Paste tens of thousands of words of legal documents]" (O1/O3-mini can efficiently handle such a long input)

Reasoning ability and logical deduction

• Depth of reasoning: O1 and O3-mini forSystematic, multi-step reasoningOptimized. They do "think longer" before answering, which produces more accurate solutions on complex tasks. For example, O1-preview solved 831 TP3T on a challenging math exam (AIME), while GPT-4o had a solution rate of 131 TP3T, which is a testament to its superior logical deduction skills in the professional world. These models perform chains of thought internally and even self-check their work.The GPT-4o is also powerful but tends to be more straightforward in generating answers; without explicit prompts, it may not perform exhaustive analyses, which can lead to errors in very complex situations that can be captured by O1.
• Handling complex tasks vs. simple tasks: Since the O1 family of models defaults to deep reasoning, they perform well on complex problems with many reasoning steps (e.g., multifaceted analysis, long proofs). In fact, in tasks requiring five or more inference steps, inference models like O1-mini or O3 outperform GPT-4 by more than 161 TP3T in accuracy. However, this also means thatFor very simple queries, O1 may "overthink". It was found that in simple tasks (less than 3 reasoning steps), O1's additional analytical processes may be a disadvantage - in many of these cases, it did not perform as well as the GPT-4 due to overreasoning. GPT-4o may answer a simple question more directly and quickly, whereas O1 may generate unnecessary analysis. The key difference is that O1 is calibrated for complexity, so it may be less efficient for trivial quizzes.Tip Example:
  • Complex tasks (suitable for O1): "Analyze and summarize the long-term impacts of climate change on the global economy, including potential risks and opportunities for different industries, the job market, and international trade."
  • Simple tasks (suitable for GPT-4o): "How's the weather today?"
• Logical deduction style: When dealing with puzzles, deductive reasoning, or step-by-step problems, the GPT-4o usually needs to hint at the works to step through them (otherwise it may jump to the answer.) The O1/O3-mini deal with logical deduction in a different way: they simulate internal dialog or drafts. For the user, this means that the O1Final answers are often well reasoned and less prone to logical gapsIt actually completes the "chain of thought" internally to scrutinize consistency. It actually completes the "chain of thought" internally to double-check for consistency. From a cueing perspective, you usuallyNo need to tell O1 to explain or check its logic--It does this automatically before presenting the answer. For GPT-4o, you might include instructions such as "first list hypotheses, then draw conclusions" to ensure logical rigor; for O1, such instructions are often redundant or even counterproductive.Tip Example:
  • GPT-4o: "Solve this logic puzzle: [puzzle content]. Show your solution step by step and explain the reasoning behind each step."
  • O1: "Solve this logic puzzle: [puzzle content]." (O1 will automatically reason logically and give a well-reasoned answer)

Response characterization and output optimization

• Details and redundancy: Due to their deep reasoning, O1 and O3-mini are usually generated for complex queriesDetailed, structured answers. For example, O1 might break down the math solution into multiple steps or provide justification for each part of the strategic plan. On the other hand, GPT-4o may default to providing more concise answers or high-level summaries unless prompted for a detailed description. In terms of prompt engineering, this meansO1 responses may be longer or more technical. You can control this redundancy better with directives. If you want O1 to be concise, you have to tell it explicitly (as you did with GPT-4)-otherwise, it may tend to be exhaustive. Conversely, if you want the output ofexplain step by step, GPT-4o may need to be told to include one, while O1 will be happy to provide one if asked (and may have done the reasoning internally anyway).Tip Example:
  • Request for Detailed Explanation (GPT-4o): "Explain in detail how the Transformer model works, including the specific roles of each component, and use technical terminology whenever possible."
  • Succinct answers are required (O1): "Summarize the core idea of the Transformer model in three sentences."
• Accuracy and self-checking: The inference model exhibits aSelf-fact-checkingOpenAI notes that O1 is better at catching its own mistakes during response generation, thus improving factual accuracy in complex responses. GPT-4o is usually accurate, but without guidance, it can occasionally be confidently wrong or hallucinate. O1's architecture reduces this risk by validating details while "thinking". In fact, users have observed that O1 produces fewer incorrect or meaningless answers to tricky questions, whereas GPT-4o may require cueing techniques (e.g., asking it to criticize or validate its answers) to achieve the same level of confidence. This means that you can usually trust O1/O3-mini to answer complex questions correctly by direct prompting, whereas with GPT-4 you may need to add instructions such as "check that your answer is consistent with the above facts". Nevertheless, no model is absolutely reliable, so always review key factual outputs.Tip Example:
  • GPT-4o (emphasizing accuracy): "Analyze the data in this financial report and calculate the company's net profit margin. Be sure to double-check the numbers to make sure the calculations are accurate."
  • O1 (default trust): "Analyze the data in this financial report and calculate the company's net profit margin."
• Speed and cost: One notable difference is that the O1 model is slower and more expensive for deeper reasoning.O1 Pro even includes a progress bar for long queries.GPT-4o tends to be more responsive for typical queries.O3-mini was introduced to provide aFaster and more cost-effective inference models--it is much cheaper per token than O1 or GPT-4o and has lower latency. However, O3-mini is a smaller model, so while it is powerful for STEM reasoning, it may not be able to match the full O1 or GPT-4 for general knowledge or extremely complex reasoning. In order to provide the bestresponsivenessWhen doing hint engineering, you need to balance depth with speed: O1 may take longer to answer thoroughly. If latency is an issue and the task is not of maximal complexity, then O3-mini (or even GPT-4o) may be a better choice.OpenAI's guidance is that GPT-4o is "still the best choice for most hints", and mainly uses O1 for really difficult problems. In short, use the right tool for the job - if you use O1, expect longer response times and plan for its slower output (possibly by notifying the user or adjusting the system timeout).Tip Example:
  • Speed priority (suitable for GPT-4o or O3-mini): "Quickly summarize the main points of this article, the quicker the better."
  • Depth Priority (suitable for O1):"Analyze the logic and evidence of this article's argument in depth and assess the credibility of its arguments."

Tips for maximizing performance engineering techniques

Effective utilization of O1 and O3-mini requires a slightly different approach to cueing than GPT-4o. The following are key cueing engineering techniques and best practices that can be used to obtain optimal results from these inference models:

Keep tips clear and minimized

Make your request succinctly and clearly. Because O1 and O3 perform intensive internal reasoning, they are not useful for focused questions or instructions without irrelevant textResponse Optimization. openAI and recent research suggests that using overly complex or leading cues for these models should be avoided. In practice, this means that you shouldState the problem or task clearly and provide only the necessary detailsNo need to add "modifiers" or rephrase the query multiple times. There is no need to add "modifiers" or restate the query multiple times. For example, instead of writing, "In this challenging puzzle, I want you to carefully reason through each step to arrive at the correct solution. Let's break it down step-by-step ......", you should simply ask: "Solve the following puzzle [including puzzle details]. Explain your reasoning." The model will naturally think internally step-by-step and give an explanation. Too many instructions can actually make thingscomplicate--A study found that adding too much cue context or too many examples canReduced performance of O1, essentially crushing its reasoning process.Tip: For complex tasks, start with zero sample hints (task descriptions only) and add more instructions only when you find that the output does not meet your needs. Often, minimizing hints produces the best results for these inference models.

Tip Example:

• Simple tips (O1/O3-mini): "Analyze this market research report to identify the three most important market trends."
• Redundancy Tip (not recommended): "I have here a very important market research report with a lot of content and a lot of information, and I want you to read it carefully and thoughtfully, think deeply about it, and then analyze it, what are the most important market trends in this report? It would be best to list the three most important trends and explain why you think they are the most important."

Avoid unnecessarily small sample examples

Traditional GPT-3/4 cueing works typically use fewer sample examples or demonstrations to guide the model. However, for O1/O3, less is more.The O1 series is specifically trained not to include cues with a large number of examples. In fact, using multiple examples wouldjeopardizePerformance. Research on O1-preview and O1-mini suggests that sample less hints consistently degrade their performance - even carefully chosen examples can in many cases make them worse than simple hints. Internal reasoning seems to be distracted or limited by the examples.OpenAI's own guidelines are in line with this: they recommend limiting the inference model to other contexts or examples to avoid confusing its internal logic. Best practice: Use zero samples or at most one example that is absolutely necessary. If you include an example, make it highly relevant and simple. For example, in legal analysis prompts, you typicallywill not (act, happen etc)Add the full example case study in advance; instead, just ask for the new case directly. The only case you might use a demo is if the task format is very specific and the model doesn't follow the instructions - then show a short example of the desired format. Otherwise, trust the model to figure it out from the direct query.

Tip Example:

• Zero sample tips (optimal): "Based on the following medical record information, diagnose a disease that the patient may have. [paste medical history information]"
• Less sample tips (not recommended): "Here are some examples of disease diagnoses:[Example 1], [Example 2] Now, please diagnose a disease that the patient may have based on the following medical record information. [paste medical history information]" (for O1/O3-mini, zero-sample prompts usually work better)

Setting roles and formats using system/developer commands

explicitcommand contextHelps guide the model's response. Use the API (or system messages in a dialog) to succinctly define the role or style of the model. For example, a system message might be, "You are a professional scientific researcher who specializes in explaining solutions step-by-step." O1 and O3-mini respond well to such role directives and incorporate them into their reasoning. However, keep in mind that they are already good at understanding complex tasks, so your instructions should focus on theThe type of output you want** instead of theHow to Think. Good uses of the System/Developer Directive include:**

• Define the scope of the task or role: Examples include "acting as a legal analyst" or "solving problems like a math teacher explains to a student". This affects the tone and level of detail.
• Specifies the output format: If you need the answer in a structured form (bulleted, table, JSON, etc.), please specify this explicitly.O1, and especially O3-mini, support structured output modes and will honor format requests. For example, "Provide your findings in the form of a list of key bullets." Given their logical nature, they tend to follow formatting instructions exactly, which helps keep responses consistent.
• Setting the boundaries: If you want to control redundancy or focus, you can include things like "provide brief conclusions after detailed analysis" or "use only the information provided and make no outside assumptions". Reasoning models will adhere to these boundaries and can be prevented from going off topic or creating illusions. This is important because O1 can produce very detailed analyses - which is usually fine, but not if you explicitly need a summary.

Make sure to include any guidance on tone, characterization, and formatting every time.

Example of a prompt (system message):

• System Message: "You are a senior legal advisor who specializes in analyzing complex legal cases and giving professional, rigorous legal advice."
• User Tip: "Analyze the case 'Smith v. Jones' and determine whether Jones should be held liable." (The model will be analyzed in the role and tone of a legal advisor)

Control of redundancy and depth through commands

While O1 and O3-mini naturally reason in depth, you can control that reasoning in theexportsThe extent to which it is reflected in the If you want toFor a detailed explanation**, prompt it (e.g., "Show your step-by-step reasoning in your answer"). They do not need to pushcarry outreasoning, but if you want tosee thatit, they do need to be informed. Instead, if you find that the model's answer is too lengthy or technical for your purposes, instruct it to be more concise or to focus on only certain aspects. For example, "Summarize the analysis in 2-3 paragraphs, including only the most critical points." Models typically follow such instructions regarding length or focus. Keep in mind that the default behavior of O1 is thoroughness - it is optimized for correctness rather than brevity - so it may tend to provide more detail. In most cases, a direct requirement for brevity will override this tendency. **

with regards toO3-mini**, OpenAI provides an additional tool to manage depth:"Strength of reasoning" parameter(Low, Medium, High). This setting lets the model know how hard it is to "think". In terms of hints, if you use an API or a system that exposes this functionality, you can turn it up for very complex tasks (ensuring maximum reasoning, but at the cost of longer answers and delays) or turn it down for simpler tasks (faster, more streamlined answers). This is essentially another way to control redundancy and thoroughness. If you don't have direct access to this parameter, you can simulate it by explicitly stating "give quick answers, no deep analysis required".low intensitymodel for situations where speed is more important than perfect accuracy. Instead, to simulatehigh intensityYou can say "Take all necessary steps to arrive at the correct answer, even if the explanation is long." These hints are consistent with the way the model's internal settings work. **

Tip Example:

• Control of redundancy: "Summarize the main points of this article with a word limit of 200 words."
• Control depth: "Analyze the argumentative structure of this essay in depth and assess whether it is logically sound and well argued."

Ensure accuracy in complex tasks

In order to get the most accurate response on difficult issues, pleaseTake advantage of the inference model in the prompt**. Since O1 can self-check and even spot contradictions, you can ask it to take advantage of this: e.g., "Analyze all the facts and double-check your conclusions for consistency."Usually it does this without prompting.Libyan Arab JamahiriyaintensifyThis command prompts the model to be extra careful. Interestingly, since O1 is already self-fact-checking, you rarely need to prompt it to "validate each step" (which is more helpful for GPT-4o). Instead, focus on providing complete and clear information. If there are potential ambiguities in the question or task, clarify them in the prompt or instruct the model to list any assumptions. This prevents the model from incorrectly guessing. **

Processing of sources and data: If your task involves analyzing given data (such as summarizing a document or calculating an answer based on the numbers provided), make sure that you present that data clearly.O1/O3-mini will do its job with it. You can even break the data down into bulleted lists or tables to improve clarity. If the model must not create illusions (e.g., in a legal context, it should not make up laws), make it clear that "Your answer is based only on the information provided and common sense; do not fabricate any details." Reasoning models are often good at sticking to known facts, and such instructions further reduce the likelihood of hallucinations.Iteration and validation: If the task is critical (e.g., complex legal reasoning or high-stakes engineering calculations), prompt engineering techniques areintegrated (as in integrated circuit)The model's response. This is not a single prompt, but a strategy: you can run the query (or ask the model to consider alternative solutions) multiple times and then compare the answers. the randomized nature of O1 means that it may explore a different path of reasoning each time. By comparing outputs or asking the model to "reflect on the existence of alternative explanations" in subsequent prompts, you can increase confidence in the results. While GPT-4o also benefits from this approach, it is particularly useful for O1 when absolute accuracy is critical - essentially leveraging the model's own depth through cross-validation.

Finally, remember that model selection is part of cueing engineering: if the problem does not actually require O1-level reasoning, it may be more efficient and just as accurate to use GPT-4o. openAI suggests reserving O1 for difficult cases and using GPT-4o for the rest. so a meta-tip: assess the task complexity first. If it is simple, either cue O1 very directly to avoid overthinking or switch to GPT-4o. if it is complex, use the techniques described above to leverage O1's capabilities.

Tip Example:

• Emphasis on data sources: "Based on the following sales data table, analyze the product categories with the fastest sales growth in the last quarter. [paste sales data table] Be sure to use only the data in the table for your analysis and do not refer to other sources."
• Iterative validation: "Analyze the case 'Smith v. Jones' and determine whether Jones should be held liable. Please give the results of your initial analysis. Then, please revisit your analysis and consider whether there are other possible explanations or loopholes. Finally, please synthesize the results of both analyses and give your final legal opinion." (Improving the reliability of legal analysis through iteration and reflection)

How O1/O3-mini handles logical deduction vs. GPT-4o

These reasoning models deal with logic problems in a fundamentally different way than the GPT-4o, and your prompting strategy should be adjusted accordingly:

• Internal Chain of Thought: O1 and O3-mini effectively perform internal dialog or step-by-step solutions as they interpret answers. Unless explicitly instructed, the GPT-4o may not go through each step rigorously. For example, in logic puzzles or math word problems, the GPT-4o may give a quick answer that sounds plausible but skips some of the reasoning, increasing the risk of error.The O1 will automatically break down the problem and consider all angles before giving an answer, which is why it earns significantly higher scores on logic-heavy assessments.Hint difference: don't prompt O1 to "show deduction" unless you actually want to see it. For GPT-4o, you'll use the CoT prompt ("First, consider ...... then ......") to improve the deduction, but for O1, it's built-in to tell it externally! Doing so may be redundant or even confusing. Instead, just make sure to state the problem clearly and then let O1 deductively reason about it.Tip Example:
  • GPT-4o (need to guide the chain of thought): "Solve the following math application problem: [APPLICATION TOPIC]. Follow these steps to solve the problem: 1. understand the meaning of the problem; 2. analyze the known and unknown conditions; 3. list the steps to solve the problem; and 4. calculate the answer."
  • O1 (no boot): "Solve the following math application problem: [Application Title]." (O1 will automatically reason logically and give the answer)
• Dealing with Ambiguity: In a logical deduction task, the GPT-4o may make immediate assumptions if there is a lack of information or ambiguity. Because of its reflective approach, O1 is more likely to mark ambiguities or consider multiple possibilities. To capitalize on this, your prompt to O1 could be to ask directly, "If there is any uncertainty, please state your assumptions before resolving them." The GPT-4 may require more of this kind of push. o1 may do this naturally, or at least be less likely to assume facts that are not given. Thus, in comparing the twoO1's rendition is cautious and thorough, and the GPT-4o rendition is quick and extensive. Adjust your cues accordingly - with GPT-4o, guide it discreetly; with O1, you mostly need to provide information and let it do its thing.Tip Example:
  • O1 (dealing with ambiguity): "Analyze this contract and determine if it is valid. If, in the course of your analysis, you find ambiguities in any of the terms, clearly identify them and state your understanding and assumptions about those ambiguities."
• Progressive export: Sometimes you actually want to be inexportsSee the logical steps in (for teaching or transparency). With GPT-4o, you must explicitly request ("Please show your work"). If the question is sufficiently complex, O1 may include structured reasoning by default, but usually it will provide a well-reasoned answer without having to explicitly enumerate each step unless asked. If you want O1 to output a chain of logic, just instruct it - it will do so without difficulty. In fact, it has been noted that O1-mini is able to provide step-by-step decompositions when prompted (e.g., in coding problems). Also, if you(negative prefix)If you want O1 to provide a lengthy exposition of the logic (perhaps you just want the final answer), you should say "give the final answer directly" to skip the detailed explanation.Tip Example:
  • Requires step-by-step output (O1): "Solve this programming problem: [Programming Problem Description]. Show your solution step-by-step, including each line of code you wrote, and explain what the code does."
  • Requires direct output (O1): "Solve this programming problem: [programming problem description]. Please give the final program code directly without explanation."
• Logical rigor vs. creativity: Another difference: GPT-4 (and 4o) is characterized by creativity and generativity. Sometimes in logic problems, this can lead to it "imagining" scenarios or analogies, which is not always necessary. o1 is more rigorous and will stick to logical analysis. If your prompt involves a scenario that requires both deduction and a bit of creativity (e.g., by piecing together clues), then you may be able to use it as an example.cap (a poem)add narration to solve a mystery), GPT-4 may be better at handling narration, while O1 will focus strictly on deduction. In the prompt project, you can combine their strengths: use O1 to get a logical solution, then use GPT-4 to embellish the presentation. If sticking with just the O1/O3-mini, be aware that you may need to explicitly ask it for creative touches or more imaginative responses - they are designed to prioritize logic and correctness.Tip Example:
  • Emphasis on Creativity (GPT-4o): "You are asked to play the role of a detective and reason out a compelling detective story based on the following clues, including the cause, course, and outcome of the case, as well as the murderer's motives and modus operandi. [provide clues]"
  • Emphasize logical rigor (O1): "You are asked to play the role of a logician who, based on the following clues, rigorously deduces the truth of the case and explains the logical basis for each step of reasoning. [provide clues]"

Key adjustments: In short, to take advantage of O1/O3-mini's logic, provide them with the most demanding reasoning tasks as individual well-defined prompts. Let them complete the logic internally (they are built for this purpose) without having to micromanage their thought processes. For the GPT-4o, continue to use classical prompt engineering (breaking down the problem, requiring stepwise reasoning, etc.) to induce the same level of deduction. And always match the prompting style to the model - what might confuse GPT-4o may be just right for O1, and vice versa, due to its different method of reasoning.

Producing Effective Tips: A Summary of Best Practices

To consolidate the above into an actionable guide, here is a list of best practices when prompting the O1 or O3-mini:

• Use clear, specific instructions: State clearly what you want the model to do or answer. Avoid irrelevant details. For complex questions, direct questioning is usually sufficient (no need to use complex role-playing or multi-question prompts).
• Provide the necessary context and omit the rest: Include any domain information that the model will need (facts about the case, data about the math problem, etc.), as the model may not have up-to-date or niche knowledge. However, do not include irrelevant text or too many examples in the prompt - additional useless content mayweakeningModeling Attention.
• Minimal or no undersampling examples: By default, start with zero sample prompts. If the model misunderstands the task or format, add a simple sample as a guide, but do not add a long chain of samples for O1/O3-mini. They don't need it and may even degrade performance.
• Set the character or tone of voice if needed: Use system messages or short prefixes to put the model in the right frame of mind (e.g., "You are a senior law clerk analyzing cases.") . This especially helps with tone (formal vs. casual) and ensures domain-appropriate language.
• Specifies the output format: If you want the answer to be in a specific structure (list, outline, JSON, etc.), please explicitly inform the model. The inference model will reliably follow the formatting instructions. For example, "Give your answer in an ordered list of steps."
• Control length and details by description: If you want a short answer, make it explicit ("Answer in one paragraph" or "Answer yes/no only and explain in one sentence"). If you want an in-depth analysis, encourage it ("provide a detailed explanation"). Don't assume that the model knows the level of detail you want by default - instruct it.
• Utilizing the O3-mini's inference strength setting: When using O3-mini via the API, select the appropriate reasoning strength (low/medium/high) for the task. High provides more thorough answers (for complex legal reasoning or difficult questions) and low provides faster, shorter answers (for quick checks or simpler queries). This is a unique way to adjust the behavior of O3-mini prompts.
• Avoid redundant "step-by-step" prompts: do not add phrases or chain-of-thought commands such as "let's think this through" for O1/O3-mini; the model already does this internally. Save these tokens and use such hints only on GPT-4o, where they have an impact. An exception might be if you explicitly want the model to output each step for transparency - then you can use this in theexportsIt is required to do so, but you still don't need to tell it toPractical implementationReasoning.
• Testing and Iteration: Since these models can be sensitive to wording, if you don't get a good answer, try rephrasing the question or strengthening the instructions. You may find that small changes (e.g., asking direct questions versus open-ended prompts) produce significantly better responses. Fortunately, O1/O3-mini requires fewer iterations than older models (they often do complex tasks correctly in one sitting), but prompt tweaks can still help optimize clarity or formatting.
• Validates important outputs: For critical use cases, do not rely on a single prompt-answer cycle. Use follow-up prompts to ask the model to validate or justify its answer ("Are you confident in that conclusion? Please explain why.") , or run the prompt again to see if consistent results are obtained. Consistent and well-reasoned answers indicate that the model's reasoning is reliable.

By following these techniques, you can take advantage of the full capabilities of the O1 and O3-mini with a highly optimized response.

Applying best practices to legal case studies

Finally, let's consider how we can translate these hints engineering guidelines intoLegal Case Analysis Scenario** (as described earlier). Legal analysis is a perfect example of a complex reasoning task in which O1 can be very effective, provided we craft the prompt:**

• Constructed Input: Begin by clearly summarizing the key facts of the case and the legal questions to be answered. For example, list the background facts as bullet points or short paragraphs, and then explicitly ask the legal question, "In light of the above facts, please determine whether Party A is liable for breach of contract under U.S. law." Constructing the prompt in this manner allows the model to parse the scenario more easily. It also ensures that no critical details are missed or overlooked.
• Provide the relevant context or law: If specific statutes, case precedents, or definitions are relevant, include them (or their excerpts) in the prompt.O1 does not have a browse function and may not be able to recall niche laws from memory, so if your analysis depends on the text of a specific law, provide it to the model. For example, "Based on [excerpt from statute X], [provide text] ...... applies this statute to the case." In this way, the model has the tools it needs to make accurate inferences.
• Set the role in a system message: System instructions such as "You are a legal analyst who explains the application of the law to the facts in a clear, step-by-step manner". will prompt the model to produce a formal, reasoned analysis. Although O1 has attempted careful reasoning, the instruction aligns its tone and structure with what we would expect in legal discourse (e.g., citing facts, applying law, drawing conclusions).
• No need for multiple examples: Do not provide a full example case study as a prompt (you may want to consider using GPT-4o for this).O1 does not need to follow the example - it can perform the analysis from scratch. However, you might briefly mention the required format, "Provide your answer in IRAC format (question, rule, analysis, conclusion)." This formatting note provides a template without the need to display lengthy examples, and O1 will organize the output accordingly.
• Control redundancy as needed: If you need an exhaustive analysis of the case, have O1 output its comprehensive reasoning. The result may be several paragraphs that cover each issue in depth. If you find the output to be too lengthy, or if you specifically need a succinct summary (e.g., a quick advisory opinion), instruct the model to "keep the analysis to a few key paragraphs, focusing on the core issues." This will ensure that you get only the main points. On the other hand, if the initial answer seems too short or superficial, prompt again with, "Explain in more detail, especially how you apply the law to the facts." The O1 will be happy to elaborate, as it has already done the heavy reasoning internally.
• Accuracy and logical consistency: Legal analysis requires accuracy when applying rules to facts. With O1, you can trust it to solve problems logically, but it's wise to double-check any legal references or specific statements it makes (since its training data may not have every detail). You can even add a hint at the end, such as, "Double-check that all the facts have been resolved and that the conclusions are in accordance with the law." Given O1's tendency to self-check, it may itself point out whether something doesn't hold up or whether other assumptions are needed. This is a useful safety net in areas where nuance matters.
• Use follow-up queries: It is common in legal scenarios to ask follow-up questions. For example, if O1 gives the analysis, you might ask, "What if the contract had different terms for termination? How would that change the analysis?" O1 can handle these iterative questions very well with reasoning. Keep in mind that if you're working on a project, the interface has no long-term memory beyond the current context of the conversation (and is not browsed), and each subsequent piece of content should rely on the context provided or include any new information needed. Keep the dialog focused on the facts of the case at hand to prevent confusion.

By applying these best practices, your tips will guide the O1 or O3-mini in delivering high-quality legal analysis. In short, present cases clearly, assign tasks, and let the reasoning model do the heavy lifting.The result should be a well-reasoned, step-by-step legal discussion that makes use of O1's logical abilities, all optimized by effective cue construction.

Using OpenAI's inference models in this way allows you to take advantage of their strengths in complex problem solving while maintaining control over the style and clarity of the output. As OpenAI's own documentation points out, the O1 series excels at deep reasoning tasks in areas such as research and strategy - legal analysis benefits from this feature as well. By understanding the differences with GPT-4o and adjusting your prompting methods accordingly, you can maximize the performance of the O1 and O3-mini and get accurate, well-structured answers, even for the most challenging reasoning tasks.