Overview
Creating effective LLM columns requires thoughtful planning before you ever start writing prompts. Taking time to clarify your goals, understand your data, and consider your workflow will ensure your AI analysis delivers the insights you actually need.
Define Your Goal
What exactly are you trying to achieve? If you don't have a well-defined goal, you won't be able to effectively prompt the AI (or a human) to complete the task. Be specific about success criteria.
Examples:
- ❌ "Analyze customer messages"
- ✅ "Identify customers at high risk of churning within 30 days based on sentiment and specific complaint types"
Plan Your Outputs
What data do you need back? Decide your output format ahead of time, so you don't accidentally end up with data that's missing key information you'll need later.
Consider:
- What fields will downstream systems require?
- What format makes analysis easiest?
- What level of detail do you need?
Assess Your Input Data
What data is necessary to achieve your goal? Ensure it's accessible and understand any quality issues:
- Is all required data available in your warehouse?
- Are there common missing values or formatting inconsistencies?
- Do you understand what each field represents?
Identify Edge Cases and Accuracy Requirements
What unusual scenarios might occur? Consider data anomalies and business-specific edge cases that could affect results.
How precise do results need to be? Your accuracy threshold affects prompting strategy - higher precision requirements need more conservative, detailed instructions.
Consider Your Workflow
Who will use these outputs? Understanding the end use case helps determine output format and confidence requirements:
- Other automated systems (need structured, predictable formats)
- Human review (can handle some ambiguity, benefit from confidence scores)
- Executive reporting (need high confidence, clear summaries)