How do you guys actually get decent reasoning chains out of DeepSeek R1 for complex data analysis without it just hallucinating halfway through? I am under a massive time crunch here because my senior project is due in three weeks and I am currently sitting in the university library in London with absolutely zero budget to burn on premium API credits, so I am stuck using the free tier/local models. I have been scouring Reddit and some developer forums all night and I keep seeing people swear by chain-of-thought prompting where you just tell the model to think step by step, but honestly, every time I try that it just gets stuck in a loop or gives me super generic fluff that doesnt actually solve my dataset problems. I read one post saying you should use XML tags like to force the model to output its logic but then another guy said that breaks the R1 internal reasoning process so now I am totally lost on what actually works. My use case is basically processing messy historical demographic data and trying to pull correlations that arent immediately obvious, and right now the model is just spitting out surface-level summaries instead of doing the actual deep dive analysis I need. Is it better to be super verbose with my instructions or should I just give it a very short direct command and let the model do its own thing? I feel like every time I add more constraints it just confuses the reasoning output and makes it lazy. Im getting really stressed because if I cant get this to work by tomorrow morning I have to manually code these correlations and there is just no way I can finish that in time. Does anyone have a reliable template or a specific way they structure their prompts for this model specifically, because the standard stuff I use for GPT-4 just isnt cutting it with R1...