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.
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.
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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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