Yo, I totally get it. I was wrestling with some complex Python refactoring using DeepSeek-R1-Distill-Qwen-32B and it kept skipping the 'why' behind its changes, which was sooo frustrating. I tried the senior dev persona too, but it basically just made the output wordier without actually making the logic deeper. Here's what I recommend: I found that minimalist prompts work best for this model. I switched to using: 'Analyze the task. In your thought process, explicitly look for logical contradictions and simulate edge cases before outputting the final result.' This makes the DeepSeek-R1 671B API version absolutely shine. It forces the model to actually use that RL-trained self-correction instead of just rushing to the code. Plus, it's SO much more reliable for multi-step puzzles cuz it doesn't get lost in persona fluff. TL;DR: Don't over-prompt the 'who' (persona), prompt the 'how' (logical checklist). It saves tokens and gets way more rigorous results. ngl, I'm super satisfied with how it handles self-correction now. gl! 👍

Respectfully, I'd consider another option. Ngl, heavy system prompts for DeepSeek-R1 usually just waste ur tokens and money. I've found it's actually MORE effective to keep instructions minimal. If you force a persona, it spends its 'thought' budget on the act instead of the logic. Just tell it to 'identify edge cases' in the user message—it's way cheaper and keeps the reasoning focused on the actual code. Peace.

bump

I totally agree that minimalist is the way to go. Honestly, looking at the current market, models like DeepSeek-R1 are a different breed compared to OpenAI o1-preview or Claude 3.5 Sonnet. Those models often need a bit of a "nudge," but R1's reinforcement learning is already pretty much optimized for the reasoning part. I'm always a bit wary that too much prompting will actually confuse the self-correction phase and make the output less reliable. If you want to keep it rigorous and safe for your Python architecture, I've found this works best:
- Keep the system prompt strictly for constraints: "Ensure all logic is verified against specific architectural patterns. Flag any potential memory leaks or race conditions."
- Lower your temperature to around 0.5 or 0.6. I've noticed R1 gets way more prone to looping or repeating itself at higher settings compared to some of the Western competitors.
- Use the system prompt to define the *output format* (like JSON or specific headers) rather than trying to dictate the *thinking style*. It’s basically about trusting the model's internal logic instead of trying to micromanage it. Just be cautious not to over-constrain the instructions, or you might actually lose that depth you're looking for.

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Wow ok that changes things. Gonna have to rethink my approach now.

Any updates on this?

Honestly, I see why everyone is suggesting the minimalist route, but as someone just starting out with DeepSeek-R1, I am a bit more cautious. I worry that if I rely too much on a system prompt to handle everything, I might miss a logic error in my Python scripts. I prefer a more DIY manual approach even if it is slower. I respectfully disagree that we should just let the model handle the self-correction on its own. Here is how I see the options for someone like me:

• Manual DIY prompting: I break the task into several turns, asking it to think, then explain, then code. Pros: I see every step and it feels much safer. Cons: It uses more of my time.
• Minimalist system prompts: What everyone else is doing. Pros: It is cheap and fast. Cons: I dont feel confident it caught every edge case. Has anyone else tried just skipping the system prompt entirely and just talking it through the steps? I find that doing it myself makes the reasoning feel more rigorous because I am the one checking the work as it goes... maybe that is just me being a bit paranoid though.