AI tools have become an integral part of my research workflow. Here’s how I use them effectively without losing the critical thinking that science demands.
Code Generation with GitHub Copilot
For repetitive coding tasks, Copilot is invaluable:
→ Boilerplate code — Setting up data structures, file I/O
→ Documentation — Auto-generating docstrings
→ Test cases — Suggesting edge cases I might miss
But I always review generated code carefully. AI can introduce subtle bugs.
Literature Review with Claude
Claude excels at:
→ Summarizing papers — Quick overviews of methodology
→ Explaining concepts — Breaking down complex statistical methods
→ Brainstorming — Exploring alternative approaches
ChatGPT for Writing
I use ChatGPT to:
→ Polish drafts — Improving clarity and flow
→ Check logic — Identifying gaps in arguments
→ Generate outlines — Structuring papers and presentations
What I DON’T Use AI For
→ Final decisions — AI suggests, I decide
→ Novel insights — That’s still my job
→ Statistical interpretations — Too risky for publication
AI is a tool, not a replacement for expertise. How do you use AI in your research? Let me know in the comments!
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