I’ve stopped spending endless hours slogging through papers and compiling raw data manually. Claude handles all that grunt work way faster than I ever could, so I get to spend my time picking apart its findings instead. Here’s exactly how I run things:I pull relevant papers myself and upload the PDFs straight to Claude. I skip opening every file; it reads all content, pulls datasets, contrasts experimental approaches and builds quick comparison charts.I walk it through common tricks authors pull in my field, like mixing data from mismatched testing environments to pump up results. I also decide which core metrics actually matter, then ask it to pull those exact figures across every study. Most crucial: I never take its conclusions at face value. Whenever it claims one method beats another, I push it hard for specific paper quotes, raw numbers, and opposing viewpoints to poke holes in its logic. AI nails repetitive bulk work but lacks niche field intuition. It can’t spot flawed research design or overlooked key stats—that’s where my expertise comes in. A lot of folks misunderstand this workflow: I’m not ditching critical thinking at all. AI does the boring legwork, I keep all the high-level judging. If you only copy its quick summaries, you’re barely scratching 10% of what this tool can do. At the end of the day: Claude reads all the papers, I question everything it comes up with. submitted by /u/CharitySuperb355
Originally posted by u/CharitySuperb355 on r/ClaudeCode