Core Prompting Techniques
Chain-of-Thought for Complex Tasks
For simple requests, AI works great. But for complex problems — analysis, math, multi-step logic — AI often stumbles. Chain-of-thought prompting fixes this.
The Problem with Complex Tasks
When you ask AI a complex question directly, it tries to jump straight to the answer:
Bad approach:
"Should we expand into the European market?"
AI gives you a generic answer without considering YOUR specific situation.
The Chain-of-Thought Solution
Force AI to think step-by-step before answering:
Good approach:
"Should we expand into the European market? Think through this step by step:
- First, analyze our current market position
- Then, identify specific European market opportunities
- Consider regulatory and operational challenges
- Evaluate resource requirements
- Finally, make a recommendation with reasoning"
The Magic Phrases
These phrases trigger step-by-step reasoning:
| Phrase | When to Use |
|---|---|
| "Think step by step" | General complex problems |
| "Let's work through this" | Collaborative analysis |
| "Walk me through your reasoning" | When you need to verify logic |
| "Before answering, consider..." | When specific factors matter |
Business Use Cases
Financial Analysis:
"Calculate whether this marketing campaign was profitable. Think step by step:
- First, identify all costs
- Then, calculate revenue attributed to the campaign
- Consider indirect benefits
- Determine ROI"
Decision Making:
"Help me decide which vendor to choose. Work through this systematically:
- Compare pricing structures
- Evaluate feature sets against our needs
- Consider integration complexity
- Assess long-term scalability
- Recommend with clear reasoning"
Problem Diagnosis:
"Our customer churn rate increased 15% last quarter. Let's analyze step by step:
- What changed in that period?
- Which customer segments are affected?
- What are potential causes?
- What data would confirm each hypothesis?"
When to Use Chain-of-Thought
| Good For | Not Needed For |
|---|---|
| Multi-factor decisions | Simple questions |
| Financial calculations | Creative writing |
| Root cause analysis | Content generation |
| Strategic planning | Basic summaries |
| Complex comparisons | Straightforward tasks |
Key Takeaway
When AI gives shallow or incorrect answers to complex questions, add "think step by step" or structure the reasoning yourself. This simple change dramatically improves accuracy for analysis, math, and multi-factor decisions.
Next: Learn role-based prompting to get expert-level outputs from AI. :::