Convert a vague data request into a precise analyst ticket
Vague requests like 'can you pull something on churn?' waste analyst time because they bounce back for clarification. This prompt turns an ambiguous ask into a scoped, specific ticket an analyst can act on immediately.
You are an analytics lead. I received a vague data request from a stakeholder and need to convert it into a well-scoped ticket for my data team before I hand it off.
Original stakeholder request (quote it exactly): {{ORIGINAL_REQUEST}}
Stakeholder role and team: {{STAKEHOLDER_CONTEXT}}
Deadline or urgency: {{DEADLINE}}
What I already know about their likely goal: {{ASSUMED_GOAL}}
Follow these steps:
1. Identify the three biggest ambiguities in the original request that, if left unresolved, would cause the analyst to build the wrong thing or ask for a redo.
2. For each ambiguity, propose the most reasonable default assumption and flag it clearly as an assumption.
3. Write a structured ticket with these fields:
- One-sentence objective
- Exact metric(s) to calculate (with formula where relevant)
- Filters and dimensions (time range, segments, exclusions)
- Required output format (table, chart, number in Slack, etc.)
- Definition of done
4. List the two or three questions to confirm with the stakeholder before the analyst starts, ranked by how much the answer would change the scope.
5. Estimate the level of effort: low (under 1 hour), medium (half day), or high (multi-day), and briefly justify.
Do not assume any specific data infrastructure. Keep the ticket tool-agnostic unless the stakeholder request mentioned a specific tool. {{ORIGINAL_REQUEST}}{{STAKEHOLDER_CONTEXT}}{{DEADLINE}}{{ASSUMED_GOAL}}
How to use this prompt
- Copy the prompt above (Copy button on the top-right).
- Replace each
{{VAR}}with your own value. Variables:{{ORIGINAL_REQUEST}}{{STAKEHOLDER_CONTEXT}}{{DEADLINE}}{{ASSUMED_GOAL}}. - Paste it into one of the recommended tools below.
- Iterate: tighten constraints in the prompt if the output is generic.
Why this prompt is structured this way
The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.
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