Combining Techniques
Mix techniques for maximum effect
The Problem: You've learned many prompting techniques, but using just one often isn't enough for complex real-world tasks. How do you combine them effectively?
The Solution: Combine Techniques Like a Recipe
Each technique is an ingredient: System Prompt sets context, Chain-of-Thought improves reasoning, Constraints control format, and Verification checks accuracy. The art is in knowing which to combine, in what order, and when one technique is enough.
Think of it like cooking: each ingredient serves a purpose, order matters:
- 1. Compatibility: Not all techniques mix well — choose complementary ones
- 2. Order: Context first, then reasoning, then format, then verification
- 3. Simplicity: If one technique solves the problem — don't add more
- 4. Testing: Always compare: single technique vs combination
When to Combine?
- Complex analysis: Role + Step-Back + Structured Output
- Content creation: Role + Few-Shot + Constraints + Emotion
- Code generation: System Prompt + CoT + Constraints + Negative Instructions
- Research: Generate Knowledge + CoT + Self-Consistency
Fun Fact: Studies show that combining just 2-3 complementary techniques can improve LLM output quality by 20-40% compared to a single technique, while adding more than 4 techniques often shows diminishing returns.
Try It Yourself!
Explore 5 real-world scenarios in the interactive tool below — see how techniques combine into powerful prompts.
Select a scenario and run the pipeline — see how each technique processes data and what it adds at each step.
Analyze quarterly sales report of a SaaS company
Order matters: context (role) → foundation (principles) → reasoning (CoT) → format (structure) → verification (check). Like cooking — ingredients first, then the recipe.
Try it yourself
Interactive demo of this technique
Analyze quarterly sales report of a SaaS company
Sales grew. Need to keep up the momentum and expand the customer base.
{
"summary": "MRR grew 15%, but churn increased from 3% to 7% — this is a critical anomaly",
"key_metrics": [
{"name": "MRR", "value": "$520K", "trend": "+15%", "status": "healthy"},
{"name": "Churn", "value": "7%", "trend": "+4pp", "status": "critical"},
{"name": "LTV/CAC", "value": "~3.5", "trend": "declining", "status": "warning"}
],
"anomalies": [
{"metric": "Churn", "issue": "Rise from 3% to 7%", "cause": "Likely pricing migration or product issues"}
],
"recommendations": [
"Urgent: conduct exit interviews with churned customers",
"Implement retention program (annual subscription discounts)",
"Review pricing tiers — plan may have become uncompetitive"
]
}
Combining 4 techniques (Role + Step-Back + CoT + Structured Output) transformed an empty "sales grew" into actionable analytics with a specific anomaly and recommendations.
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