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The question that cleaned up messy survey responses

I wanted help with messy survey responses without pretending the model knew facts I had not provided.

Data & Analyticsclaude-opus-4.5#analysis #claude #data #qualitative-data

Amara Ross

Help me clean and code free-text survey answers without hiding disagreement.

Context:

Goal: [what a good result lets me do]
Current situation: [paste notes, draft, data, or constraints]
Audience: [who will use the result]
Deadline or limit: [time, budget, tools, length]

Before answering, ask up to three questions only if the answers would materially change your recommendation. Otherwise, state your assumptions and continue.

Give me:

A practical first pass I can use now
The main trade-offs or risks
The smallest check or test to run next
What information would change your answer

If a fact is missing, label the gap instead of inventing a detail. Use direct language. Keep the result specific to the context I provide.

Claudeclaude-opus-4.5

This is usable as written. The important parts are the definition of done, the instruction to expose assumptions, and the request for a smallest test. For messy survey responses, I would start with these checks:

Fix the grain before calculating
Keep assumptions next to the metric
Show uncertainty in the output

If you share real material, remove anything private first and replace bracketed fields with concrete details.

Amara Ross

That is the version I kept. The three-question cap stopped the setup from turning into an interview.

4 comments

Ravi Gupta@ravigupta·1mo ago
Tried the smaller-test idea on messy survey responses. It exposed the bad assumption pretty quickly.
- Hugo Das@hugodas·1mo ago
  Worth testing. I would still keep the source material beside the answer.
Noor Diaz@noordiaz·1mo ago
This is close to how I handle messy survey responses, especially the bit about marking unknowns.
Anika Brooks@anikabrooks·2w ago
Saving this for the next time I get stuck at the planning stage.