gh-hiroshi75-ccplugins-langgraph-master-plugin/skills/fine-tune/prompt_priorities.md

Prompt Optimization Priorities

A priority guide for applying optimization techniques in order of improvement impact.

📊 Optimization Priorities

In order of improvement impact:

1. Adding Few-Shot Examples (High Impact, Low Cost)

• Improvement: Accuracy +10-20%
• Cost: +5-10% (increased input tokens)
• Implementation Time: 30 minutes - 1 hour
• Recommended: ⭐⭐⭐⭐⭐

2. Output Format Structuring (High Impact, Low Cost)

• Improvement: Latency -10-20%, Parsing errors -90%
• Cost: ±0%
• Implementation Time: 15-30 minutes
• Recommended: ⭐⭐⭐⭐⭐

3. Temperature/Max Tokens Adjustment (Medium Impact, Zero Cost)

• Improvement: Latency -10-30%, Cost -20-40%
• Cost: Reduction
• Implementation Time: 10-15 minutes
• Recommended: ⭐⭐⭐⭐⭐

4. Clear Instructions and Guidelines (Medium Impact, Low Cost)

• Improvement: Accuracy +5-10%, Quality +15-25%
• Cost: +2-5%
• Implementation Time: 30 minutes - 1 hour
• Recommended: ⭐⭐⭐⭐

5. Model Selection Optimization (High Impact, Requires Validation)

• Improvement: Cost -40-60%
• Risk: Accuracy -2-5%
• Implementation Time: 2-4 hours (including validation)
• Recommended: ⭐⭐⭐⭐

6. Prompt Caching (High Impact, Medium Cost)

• Improvement: Cost -50-90% (on cache hit)
• Complexity: Medium (implementation and monitoring)
• Implementation Time: 1-2 hours
• Recommended: ⭐⭐⭐⭐

7. Chain-of-Thought (High Impact for Specific Tasks)

• Improvement: Accuracy +15-30% for complex tasks
• Cost: +20-40%
• Implementation Time: 1-2 hours
• Recommended: ⭐⭐⭐ (complex tasks only)

8. Self-Consistency (Limited Use)

• Improvement: Accuracy +10-20%
• Cost: +200-300%
• Implementation Time: 2-3 hours
• Recommended: ⭐⭐ (critical decisions only)

🔄 Iterative Optimization Process

1. Measure baseline
   ↓
2. Select the most impactful improvement
   ↓
3. Implement (one change only)
   ↓
4. Evaluate (with same test cases)
   ↓
5. Is improvement confirmed?
   ├─ Yes → Keep change, go to step 2
   └─ No → Rollback change, try different improvement
   ↓
6. Goal achieved?
   ├─ Yes → Complete
   └─ No → Go to step 2

Summary

For effective prompt optimization:

1. ✅ Clarity: Clear role, task, and output format
2. ✅ Few-Shot Examples: 3-7 high-quality examples
3. ✅ Structuring: Structured output like JSON
4. ✅ Parameter Tuning: Task-appropriate temperature/max_tokens
5. ✅ Incremental Improvement: One change at a time, measure, validate
6. ✅ Cost-Conscious: Model selection, caching, max_tokens
7. ✅ Measurement-Driven: Evaluate all changes quantitatively