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I recently tested handing AI a data analysis on deportation data and found I preferred having it write code I could inspect over letting it do the analysis as a black box. I started with a profiling prompt in Claude, then moved to a Quarto document so I could read its reasoning next to code I could run myself.
You'll learn:
- How to prompt AI to profile a dataset before any analysis, asking what one row represents, what time period it covers, and what looks odd, while telling it not to write analysis code
- Why asking AI to just do the analysis creates a black box, and how having it write reviewable code avoids that
- How to ask AI for a Quarto document that walks through exploratory data analysis, with explanatory text and runnable code together
- A trust-but-verify workflow: run the code AI writes so you can check whether its claims about your data are accurate
- How to spot caveats in AI output, such as a codebook page that blocked automated fetches, so you know what the AI could not read
Tools mentioned: Claude, Quarto, R
Key moments:
- 00:09 Why not to ask AI to just do the analysis
- 00:48 Introducing the deportation data used in the test
- 01:04 Profiling prompt in Claude, no analysis code
- 01:50 Summary of what one row represents and the time period
- 02:35 Caveat about the codebook website blocking fetches
- 03:03 Switching to a Quarto document for the walkthrough
- 03:52 Trust but verify workflow
- 04:16 Why Quarto fits this kind of AI-assisted analysis