Reusable prompt
Create a data story with context, insight, implication, and action.
Task type: Data Storytelling
Objective: Create a data story with context, insight, implication, and action.
Context:
- [Project, product, or topic]: [Project, product, or topic]
- [Audience and situation]: [Audience and situation]
- [Constraints, must-haves, and things to avoid]: [Constraints, must-haves, and things to avoid]
Inputs to provide:
[Paste source material here]
Expected output:
1. Context
2. Findings
3. Impact analysis
4. Recommendation
5. Next steps
Quality bar:
- Be specific and avoid generic advice.
- State assumptions explicitly.
- Prefer actionable next steps over broad theory.
- If important information is missing, ask up to 3 clarifying questions before answering.
- For time-sensitive or factual claims, label what is known, inferred, and needs verification.Worked example
The example below fills the same prompt for a realistic Data & BI scenario. It is intentionally modest: the goal is to show how the prompt behaves, not to pretend one template solves every Data & BI problem.
Task type: Data Storytelling
Objective: Create a data story with context, insight, implication, and action.
Context:
- [Project, product, or topic]: A real Data & BI task using the Data Storytelling prompt
- [Audience and situation]: A teammate who needs a useful answer and clear next steps
- [Constraints, must-haves, and things to avoid]: Be specific, state assumptions, avoid unsupported claims, and keep the output easy to act on.
Inputs to provide:
Sample material: The team needs help with Data Storytelling. The current situation is messy, the goal is clear enough to start, and the answer should separate facts, assumptions, risks, and next actions.
Expected output:
1. Context
2. Findings
3. Impact analysis
4. Recommendation
5. Next steps
Quality bar:
- Be specific and avoid generic advice.
- State assumptions explicitly.
- Prefer actionable next steps over broad theory.
- If important information is missing, ask up to 3 clarifying questions before answering.
- For time-sensitive or factual claims, label what is known, inferred, and needs verification.How to use this prompt
- Replace the placeholders with the actual Data Storytelling task, audience, source material, and constraints.
- Keep the requested output sections unless you have a strong reason to remove one; they are there to make the AI answer easier to evaluate.
- Paste the finished prompt into your AI assistant, then ask one follow-up question that tests assumptions or missing evidence.
What a good answer should contain
- 1. ContextUse this section to make the answer concrete: Context.
- 2. FindingsUse this section to make the answer concrete: Findings.
- 3. Impact analysisUse this section to make the answer concrete: Impact analysis.
- 4. RecommendationUse this section to make the answer concrete: Recommendation.
- 5. Next stepsUse this section to make the answer concrete: Next steps.
Why this prompt works
- Data Storytelling starts with an explicit task type and objective, which reduces vague answers.
- It asks for context, source material, and constraints before the model writes the final response.
- The 5 output sections make the answer scannable and easier to compare across attempts.
- The quality bar tells the assistant to ask clarifying questions and mark claims that need verification.
Common mistakes to avoid
- Leaving placeholders untouched and expecting the model to infer the missing context.
- Removing the output structure, then asking for a final answer that is hard to review.
- Using the prompt for time-sensitive facts without checking sources or dates.