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Analysis: Data Interpretation

Requiring correlations, caveats, and expected impact levels produces analyst-quality output. The anti-vague language rule forces the AI to back every claim with specific numbers.

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Prompt
Analyze this {{data_type}} and extract actionable insights.

Data:
{{paste_data}}

Your analysis must include:

1. **Summary:** What does this data show at a high level? (3 sentences)
2. **Trends:** What patterns are visible over time or across segments?
3. **Anomalies:** Flag anything unexpected with a possible explanation
4. **Correlations:** What variables seem related? (note: correlation is not causation)
5. **Recommendations:** 3 specific actions based on the data, each with expected impact (high/medium/low)
6. **Caveats:** What can this data NOT tell us? What additional data would strengthen the analysis?

Present numbers with context (percentages, comparisons to benchmarks). Avoid vague language like "significant" without a number.

Variables to customize

{{data_type}}{{paste_data}}

Why this prompt works

Requiring correlations, caveats, and expected impact levels produces analyst-quality output. The anti-vague language rule forces the AI to back every claim with specific numbers.

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