Turn a vague data request into an analysis brief

Convert a stakeholder's fuzzy ask into a scoped analysis brief with the real question, definitions, data needed and success criteria.

LLazyPrompt(@lazyprompt)Data & AnalysisPlanWorks with any model
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Prompt template
I'm a data analyst who received a vague request. Help me turn it into a clear analysis brief I can confirm with the requester before I start.

The request as I received it:
{{request}}

Who asked and how they will use it: {{requester}}
Deadline: {{deadline}}
Data I think I have access to: {{data}}

Write the brief with:
1. The real question: restate the request as one precise, answerable question, plus the decision it will inform. Offer two alternative interpretations if it is ambiguous.
2. Definitions to agree on: every term that could be read differently (for example "customer", "active", "revenue", time period), with a proposed default for each.
3. Scope: what is in, what is out, and what segments or time windows will be compared.
4. Method: the analysis approach in 3-5 steps, and the single most important assumption.
5. Data needed versus data available, and the gaps and how I could work around them.
6. Deliverable: format, length, and the 1-2 charts or tables that will carry the answer.
7. Risks and what would change the conclusion.
8. Timeline: milestones that fit my deadline, with a point for an early check-in.
9. A short, friendly message I can send to the requester with the 3-4 questions that must be answered before I start.

Keep the brief to one page. Do not assume details I have not given; list them as questions.
Example output
**1. The real question**: "Has product usage by enterprise accounts declined over the last 90 days relative to the prior 90, and in which segments is the decline concentrated?" Decision: where to focus account manager capacity. Alternative readings: (a) engagement means support contact frequency; (b) lower feature adoption rather than lower logins. **2. Definitions (proposed defaults)** - Enterprise: accounts with annual contract value above $50k. - Engaged: a user with 3+ active days in a rolling 28-day window. - "Lately": last 90 days vs the previous 90. **3. Scope**: In: enterprise accounts active at the start of the period. Out: accounts that churned (reported separately). Compare by industry, account age and seat count. **4. Method**: 1) Build weekly engaged-user rate per account. 2) Compare periods and test for seasonality using last year. 3) Segment to find where the drop concentrates. Key assumption: usage logs are complete across the period. **5. Data gaps**: no seat license counts in usage logs; workaround: use CRM seats. **6. Deliverable**: 3-slide summary: trend chart, segment heatmap, account watchlist. **8. Timeline**: Day 1 confirm definitions; Day 2 data prep; Day 3 analysis and check-in; Day 4 write-up. **9. Message**: "Quick questions before I start: Is engagement about product usage? Which accounts count as enterprise? Which period is 'lately'? Is this a decision about account manager assignments?"

Why it works

Agreeing on definitions and the decision up front prevents rework and surprises. Send the drafted questions before you touch the data.

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