Ok so, I've been through this too. Basically, DeepSeek-R1 is super sensitive because its reasoning process is literally its backbone. If you try to suppress that with a rigid system prompt, it *actually* starts to hallucinate or skip steps because you're messin' with its core logic. I've found two main ways to handle this: **Option A: The Minimalist Approach**
Just tell it "You are a logical reasoning assistant." This lets it breathe, but like you said, the output can be a mess. *Pros:* Max reasoning power. *Cons:* Zero formatting control, tbh. **Option B: The Delimiter Method**
This is my go-to. I tell it: "Always perform reasoning in tags, then provide the final answer in a JSON code block." Honestly, the best system prompt I've used is: "You are an expert developer. Think through the problem step-by-step. Provide your final response in strictly valid JSON format, but ONLY after your internal reasoning." It works way better than forcing it to *only* speak JSON. Just be cautious though, cuz if the logic is too long, it might hit token limits, right? Plus, always validate that JSON on your end, it's not always 100% foolproof!! gl!

Seconding the recommendation above. I've been running DeepSeek-R1 through some heavy dev pipelines and honestly... it can be a bit of a nightmare if you're trying to keep costs down. I totally agree that stifling the reasoning is a bad move, but unfortunately, letting it ramble costs a fortune in output tokens. I had issues with the model hallucinating schema fields just to satisfy its own 'logic' chain when I used a standard system prompt. Basically, I've compared two main setups to find a balance between logic and budget: **Option A: The 'Hard Constraint' Prompt**
This is where you tell it 'You are a JSON extractor. No talking.' * **Pros:** Very cheap on tokens since it skips the fluff.
* **Cons:** Logic is terrible. It misses edge cases and honestly just fails at complex scripts. Not as good as expected. **Option B: The 'Encapsulated Reasoning' Template**
I tell it: 'Reason through the problem thoroughly, then provide the final JSON inside tags.'
* **Pros:** High accuracy. It actually catches those edge cases you mentioned.
* **Cons:** You're paying for those reasoning tokens. But it's cheaper than 5 retries! If you're using the DeepSeek-R1-67B or the full DeepSeek-R1 API, the 'Encapsulated' route is the only way to stay sane. I've found that adding 'Do not apologize or add fluff' at the end of the system prompt helps keep the final delivery clean without neutering the CoT. Ngl, it still bugs out sometimes, but it's way more reliable. Anyone else notice if it behaves better when you put the formatting rules in the *User* prompt instead of the System one? I feel like it ignores System rules once it gets deep into a thought... but idk. Good luck!! 👍

.

Similar situation here - I went through this last year when I was trying to pipe R1 outputs into a production pipeline. I totally agree with the first reply that trying to stifle the reasoning is basically asking for trouble. One small point I'd add is that the model's temperature settings seem to interact weirdly with complex system prompts, making it even more unpredictable if you crank it too high. Basically, I compared three different approaches during my journey: 1. **System-Level Constraints:** I tried to hardcode the "personality" and JSON rules in the system prompt. Pros: Consistent tone. Cons: It honestly felt like it was fighting its own logic, leading to weird loops and hallucinations. 2. **User-Level Formatting:** I moved all the formatting rules to the end of the user message instead. Pros: Much better adherence. Cons: The reasoning could still get a bit "leaky" into the final code block.
3. **The "Let It Cook" Method:** I let the reasoning run wild and just used a specific marker for the final answer. Pros: Highest logic accuracy and caught every edge case. Cons: Longer token usage and higher cost. For me, the third option was the clear winner. It was a real journey of trial and error. I was so frustrated at first cuz it kept ignoring my schemas, but once I realized that R1 NEEDS that internal monologue to stay grounded, everything clicked. I'm super satisfied now. I've learned to be really cautious about over-prompting—sometimes less is actually MORE with this model. Just sharing my experience... it's definitely a beast to tame! gl!

Same setup here, love it

omg i am literally having the exact same problem right now!! it is so amazing to see someone else struggling with this because i thought i was just doing it wrong...

• been stuck on this for like three weeks and honestly nothing i try actually fixes the formatting
• i am super worried about making the model less reliable if i mess with the system instructions too much because safety is huge for me @Reply #3 - good point! the extra tokens from all that rambling are making my wallet cry and i still cant find a way to stop it. ngl i am just totally lost at this point!