Harness the Power of o1 & Reasoning Models
o1 (and other next-generation reasoning models) can sometimes feel slower or more complex, but they excel at handling large contexts, autonomous planning, and delivering thorough, well-reasoned responses at scale. Below are summarized best practices and prompt patterns gathered from various experts and community members, including swyx and Ben Hylak.
3 Key Observations
1. o1 isn’t a chat model
o1 is more of a “report generator”—it benefits from thorough, upfront context rather than iterative chat. If you only give it a quick question, it won't pull the information it needs from you.
2. Focus on desired output (the "what"), not the "how"
Don’t micromanage or replicate chain-of-thought. Instead, define exactly the deliverable you need (like a new file or architecture) and let o1 figure out its process autonomously.
3. Know o1’s strengths and weaknesses
o1 is excellent at generating large, correct files in one shot, delivering thorough explanations, and providing high-level reasoning. However, it's not (yet) great at creative writing or crafting custom styles.
Detailed Prompting Tips
- Use direct, minimal prompts: Avoid extraneous details; keep prompts clear and concise.
- Include built-in verification: Ask for solutions, then instruct the model to double-check or verify.
- Avoid few-shot examples: o1 typically does better with zero-shot or minimal example usage.
- Avoid explicit chain-of-thought: Overly detailed step-by-step instructions can sometimes confuse o1.
- Encourage deeper reasoning: Phrases like “Provide a detailed analysis” or “Take as much time as you need” help o1 produce thorough responses.
- Structure the prompt with lists: Provide enumerated or bullet-listed instructions to get organized, systematic answers.
- Apply specific constraints: Define success criteria (budget, timeline, rules) so o1 can measure and refine its output.
- Encourage multiple perspectives: Request multiple solution paths or approaches and have o1 compare them.
- Prompt the model to self-critique: E.g., “Review your solution and outline any potential errors.”
- Be explicit about final output: If you want short bullet points or a single code file, specify so clearly.
- Provide factual data: If accurate facts are needed, feed relevant data or context into the prompt.
Additional Insights
Provide more context than you think you need: o1 rarely asks clarifying questions; it relies on you to push context to it. Treat it like onboarding a new team member. The more relevant details it has, the more accurately it can solve complex tasks.
Check for drift in style or voice: Because o1 tends to adopt a formal, report-like tone, explicitly state if you want a friendlier, more casual style. You may need to refine your prompt to break from the default.
Plan for latency: o1 can be slower, especially on large tasks. Consider using a background job or a notification system if you’re building an application on top of o1.
Wrapping Up
o1’s capabilities open up new ways of tackling complex tasks and thoughtful reasoning — but it requires fresh prompting strategies. Shift your mindset from chat-based Q&A toward providing thorough context, clearly-stated requirements, and minimal instructions on process. Let o1 handle the reasoning. Try these tips, experiment with your own, and find the sweet spot that fits your workflow.
Ready to try it yourself?
Provide o1 with all the relevant background info, clarify the output you need, then request multiple solutions or a self-check. With practice, you’ll experience how o1 can generate well-reasoned, single-shot answers to your most challenging problems.