Just saw this thread. Since youre running NVIDIA A100 80GB SXM4 GPU hardware, you probably care more about reliability than saving pennies on tokens, but logic drift is a real risk with these models. I have tested a few configurations for backend logic and here is how they stack up:

• Empty System Prompt: Safest for pure reasoning. It lets the model use its native weights. Problem is it gets chatty and might include 500 lines of thought process you dont need.
• Strict Format Instructions: Instead of telling it how to think, just tell it what to output. Use "Return code blocks only" or "Output in valid Python." This keeps the logic sharp without messing with the reasoning.
• Library-Specific Constraints: Just list the exact versions of your stack. If youre using DeepSeek-R1 671B BF16 or full weights, it needs to know the environment, not its "persona." Be careful with those GitHub prompts that force step-by-step thinking. R1 is already hardwired for that. Forcing it often leads to circular loops where it just repeats the same logic patterns over and over until it hits the token limit. I would suggest keeping the system prompt focused entirely on your technical requirements. If the logic starts slipping, honestly, check your temperature settings before you blame the prompt. Keep it low for migration work... like 0.1 or 0.2 max.

To add to the point above: I ran into a similar mess while migrating some legacy data pipelines a while back. I tried to force a really strict Senior Software Engineer persona in the system prompt, thinking it would make the logic tighter. Honestly, it backfired. The model spent so much energy trying to sound like a senior dev that it missed some basic race conditions in the actual script. I've found that with DeepSeek-R1 671B Full Weights, the more you try to steer it from the outside, the more likely it is to trip over its own feet. Be careful with those long step-by-step instructions in the system block because they can conflict with the internal chain-of-thought process. Quick tip: keep the system prompt empty and stick your specific constraints directly into the user message. Also, maybe double check your temp settings... keep it at 0.6 or lower for backend migration stuff so it stays grounded.

Unfortunately, my testing on NVIDIA A100 80GB Tensor Core GPU shows that complex system prompts usually interfere with the native logic of DeepSeek-R1 671B Full Weights. It isnt as good as expected when you try to force logic paths, tho.

• Use a blank system prompt for reasoning tasks.
• Define your code constraints in the user message instead. This approach usually stops the model from hallucinating.