Use Case

Data Analysis

Analyze data, generate insights, and create reports

Overview

AI can help analyze datasets, identify patterns, and generate insights. It works well with structured data, spreadsheets, and even raw text that needs quantification.

Provide clear context about your data structure and what insights you're looking for to get the most useful results.

Key Techniques

Chain-of-Thought

Request step-by-step analysis for complex datasets

Few-Shot

Show desired output format for consistent analysis

XML Tags

Separate data, questions, and output format

Prompt Chaining

Break analysis into clean → analyze → report pipeline

Examples

Example 1: Trend Analysis
Prompt
Analyze this sales data and identify: - Month-over-month growth trends - Top 3 performing products - Any seasonal patterns Data: [sales data table] Output format: Summary with bullet points and one chart recommendation
Output
Key Findings:
• 23% MoM growth (accelerating)
• Top products: Widget Pro, SuperTool, BasicKit
• Q4 shows 40% higher sales (holiday season)

Chart Recommendation: Line chart for trends, bar chart for product comparison
Example 2: Customer Feedback Analysis
Prompt
Categorize these customer reviews by sentiment and topic: 1. "Love the product but shipping was terrible" 2. "Best purchase I've made this year" 3. "Good value but needs better instructions" 4. "Customer service was incredibly helpful" Create a table with: Review, Sentiment, Topics, Action Item
Output
Review Sentiment Topics Action
1 Mixed Product, Shipping Improve shipping
2 Positive Overall Use in testimonials

Best Practices

  • 01
    Structure your data clearly

    Use tables, CSV format, or clear delimiters

  • 02
    Ask specific questions

    Instead of "analyze this," ask "what trends do you see?"

  • 03
    Request specific output formats

    Tables, bullet points, charts - specify what you need

  • 04
    Verify critical findings

    Double-check important insights with follow-up questions

← Previous: Writing & Editing Next: Research →