OCOPD When I first got my hands on the new OpenAI o1 (Strawberry) models, my instinct was to stick to my old playbook. I'd add "think step-by-step" to every complex prompt. But after testing o1 on some heavy data processing tasks, I noticed something: traditional Chain-of-Thought (CoT) prompting actually degrades o1's performance.
Why Old Prompting Tricks Fail on o1
The o1 model is trained with an internal reasoning loop. When you explicitly tell it to "think step-by-step," you're forcing an external framework over an already optimized internal process. In my tests, this caused the model to hallucinate steps or generate overly verbose, redundant logic.
My New o1 Prompting Strategy
Instead of telling the model how to think, I focus entirely on constraints and the desired output schema. Here's a comparison of how I changed my approach.
The Old Way (GPT-4 Style)
You are a Python expert. Think step-by-step.
1. Read the CSV data.
2. Filter out null values.
3. Calculate the mean.
Write the code to do this.The New Way (o1 Style)
Write a Python script that calculates the mean from a CSV.
Constraints:
- Input data may contain nulls.
- Use only standard library modules (no pandas).
- Output must be a single float.Notice the difference? I dropped the step-by-step instructions. I just gave it the constraints. The model handles the reasoning internally and outputs a much cleaner solution.
Handling Formatting with o1
One quirk I found is that o1 sometimes struggles with strict JSON output if its internal reasoning gets too long. To fix this, I append a strict formatting rule at the very end of the prompt.
... [task instructions] ...
CRITICAL: Return ONLY valid JSON. No markdown formatting, no explanations.Testing It Yourself
If you're migrating from GPT-4o to o1, I recommend stripping all your prompts of words like "reason through this," "explain your thought process," and "step-by-step." Keep it direct, heavily constrained, and let the model do the heavy lifting.