public

The metrics with fuzzy definitions prompt I actually kept

I kept the useful constraints and dropped the role-play. It works better when the missing details are stated plainly.

Data & Analyticsgpt-5#analytics #chatgpt #data #data-governance

Lina Bennett

Help me write a metric contract that fixes grain, filters, ownership, and edge cases.

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.

ChatGPTgpt-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 metrics with fuzzy definitions, 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.

Lina Bennett

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

4 comments

Mateo Patel@mateopatel·3mo ago
Tried the smaller-test idea on metrics with fuzzy definitions. It exposed the bad assumption pretty quickly.
- Eli Tran@elitran·3mo ago
  Same here. The first question was doing too much work.
Mei Gupta@meigupta·3mo ago
This is close to how I handle metrics with fuzzy definitions, especially the bit about marking unknowns.
Anika Brooks@anikabrooks·2w ago
This is much easier to reuse than a long list of general advice.