How to Prompt OpenAI o1 Models Effectively

TL;DR

Prompt OpenAI o1 models with short, direct requests that state the desired outcome and include only the necessary context. Avoid asking o1-preview or o1-mini to think step by step because they already perform iterative reasoning internally; instead, use markdown, XML, or delimiters to isolate relevant excerpts. Read on for five core prompting practices and guidance on choosing o1-preview, o1-mini, or GPT-4o-family models.

Transcript

everything you know about prompt engineering is pretty much obsolete I'm just kidding well sort of these new open AI models Mark the beginning of the end of the old way of prompting it's not that the language of prompting has changed but there's a brand new dialect and I think that this new dialect will start to take over more and more over the nex... Read More

Key Insights

• Concise prompting is the recommended approach for o1 models because users need less qualification and spoon-feeding than before. A direct request such as analyzing the factors behind Rome's fall is preferred over a long introduction filled with detailed formatting and content requirements.
• Built-in reasoning is a defining feature of o1-preview and o1-mini, according to the presenter. Prompts therefore do not need phrases such as "think step by step" or requests for a detailed chain of thought, because the models already iterate internally against the user's goal and requirements.
• Clear delimiters help o1 models distinguish instructions from source material. Quotation marks, hashtags, parentheses, markdown, and XML-style notation can isolate the passage that deserves attention, making a compact prompt easier for the model to interpret without adding lengthy explanatory language.
• Focused context is more effective than dumping entire documents into an o1 prompt. The presenter recommends locating the relevant paragraph or excerpt first, then supplying only that material, because these reasoning models can overthink when they receive more information than the task requires.
• System personas are unnecessary for the demonstrated o1 prompting style. Instead of declaring that the model is an expert travel guide, the user can request a seven-day Japan itinerary directly and rely on the model to infer the appropriate expertise from the desired outcome.
• Model selection works like choosing tools from a toolbox, with each model suited to different jobs. The presenter compares o1 to a hammer, GPT-4o to a frequently used wrench, and mini or older lightweight models to a screwdriver for narrower, faster tasks.
• O1-preview is best positioned for high-level strategic work in the presenter's framework. It can address broad questions about company operations, service strategy, or potential pivots, functioning more like an executive asked for direction than a worker assigned routine writing or document summarization.
• GPT-4o-family models remain the presenter's choice for 80 percent of general cases and all current production cases. Their predictable behavior, lower latency, and lower reasoning expense make them preferable when workloads scale from thousands to millions of operations.

Questions & Answers

Q: How should prompts for OpenAI o1 models be written?

Write short, direct prompts centered on the desired outcome. Remove greetings, excessive qualifications, elaborate personas, and unnecessary background, as if you were texting the model instead of writing an email or essay.

Q: What are the five core practices for prompting o1-preview and o1-mini?

Keep prompts short, avoid chain-of-thought instructions, use markdown, XML, or delimiters, provide focused context rather than entire documents, and omit unnecessary system messages. These practices reflect how the models reason internally and help them focus on the requested outcome.

Q: Why should users avoid telling o1 models to think step by step?

O1-preview and o1-mini already have built-in reasoning that iterates over an output to check whether it meets the requirements and the user's goal. A prompt can therefore state the problem directly without adding instructions to explain every reasoning step.

Q: How do delimiters improve prompts for o1 models?

Delimiters separate the instructions from the material the model should analyze. Quotation marks, hashtags, parentheses, markdown, and XML-style notation can clearly identify the exact text that deserves attention.

Q: Why should users avoid giving o1 models too much context?

The presenter says these models think extensively and can overthink when given too much information. Instead of supplying a 20-page document, find the specific paragraph or excerpt that needs analysis and provide only that focused material.

Q: Do o1 prompts need system messages or expert personas?

The demonstrated approach does not require system messages such as declaring the model a world-class writer. State the desired result directly, such as requesting a well-written essay or a seven-day Japan itinerary, and let the model infer the needed expertise.

Q: When should o1-preview and o1-mini be used?

O1-preview is recommended for broad, high-level strategic questions about areas such as company operations, service strategy, or potential pivots. O1-mini is presented as a faster option suited to math, coding, counting, and administrative tasks.

Q: Why are GPT-4o-family models recommended for production workloads?

The presenter favors GPT-4o-family models for production because they offer more predictable behavior, lower latency, and lower reasoning expense. O1 models may spend between 5 and 50 seconds reasoning and use hidden reasoning tokens, which can increase delays and costs across thousands or millions of operations.

Summary & Key Takeaways

• Effective o1 prompts are short, direct, and focused on a specific outcome. The recommended approach removes greetings, elaborate personas, excessive qualifications, and instructions to reason step by step. The model already performs iterative reasoning internally, so users can state the task plainly and trust it to infer the expertise needed for completion.

• Prompt structure still matters even when instructions become shorter. Markdown, XML, quotation marks, hashtags, parentheses, or similar delimiters can separate source material from directions and clarify what deserves attention. Rather than uploading extensive documents, users should extract the exact paragraph or passage that the model needs to analyze, reducing unnecessary overthinking and distraction.

• Model selection should reflect the task instead of treating every model as interchangeable. The presenter recommends o1-preview for broad strategic questions, o1-mini for faster mathematical, coding, counting, or administrative work, and GPT-4o-family models for most existing workflows. Production systems particularly benefit from their lower latency, predictable behavior, and more manageable operating costs.