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skills/fine-tune/prompt_optimization.md

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+# Prompt Optimization Guide
+
+A comprehensive guide for effectively optimizing prompts in LangGraph nodes.
+
+## 📚 Table of Contents
+
+This guide is divided into the following sections:
+
+### 1. [Prompt Optimization Principles](./prompt_principles.md)
+Learn the fundamental principles for designing prompts.
+
+### 2. [Prompt Optimization Techniques](./prompt_techniques.md)
+Provides a collection of practical optimization techniques (10 techniques).
+
+### 3. [Optimization Priorities](./prompt_priorities.md)
+Explains how to apply optimization techniques in order of improvement impact.
+
+## 🎯 Quick Start
+
+### First-Time Optimization
+
+1. **[Understand the Principles](./prompt_principles.md)** - Learn the basics of clarity, structure, and specificity
+2. **[Start with High-Impact Techniques](./prompt_priorities.md)** - Few-Shot Examples, output format structuring, parameter tuning
+3. **[Review Technique Details](./prompt_techniques.md)** - Implementation methods and effects of each technique
+
+### Improving Existing Prompts
+
+1. **Measure Baseline** - Record current performance
+2. **[Refer to Priority Guide](./prompt_priorities.md)** - Select the most impactful improvements
+3. **[Apply Techniques](./prompt_techniques.md)** - Implement one at a time and measure effects
+4. **Iterate** - Repeat the cycle of measure, implement, validate
+
+## 📖 Related Documentation
+
+- **[Prompt Optimization Examples](./examples.md)** - Before/After comparison examples and code templates
+- **[SKILL.md](./SKILL.md)** - Overview and usage of the Fine-tune skill
+- **[evaluation.md](./evaluation.md)** - Evaluation criteria design and measurement methods
+
+## 💡 Best Practices
+
+For effective prompt optimization:
+
+1. ✅ **Measurement-Driven**: Evaluate all changes quantitatively
+2. ✅ **Incremental Improvement**: One change at a time, measure, validate
+3. ✅ **Cost-Conscious**: Optimize with model selection, caching, max_tokens
+4. ✅ **Task-Appropriate**: Select techniques based on task complexity
+5. ✅ **Iterative Approach**: Maintain continuous improvement cycles
+
+## 🔍 Troubleshooting
+
+### Low Prompt Quality
+→ Review [Prompt Optimization Principles](./prompt_principles.md)
+
+### Insufficient Accuracy
+→ Apply [Few-Shot Examples](./prompt_techniques.md#technique-1-few-shot-examples) or [Chain-of-Thought](./prompt_techniques.md#technique-2-chain-of-thought)
+
+### High Latency
+→ Implement [Temperature/Max Tokens Adjustment](./prompt_techniques.md#technique-4-temperature-and-max-tokens-adjustment) or [Output Format Structuring](./prompt_techniques.md#technique-3-output-format-structuring)
+
+### High Cost
+→ Introduce [Model Selection Optimization](./prompt_techniques.md#technique-10-model-selection) or [Prompt Caching](./prompt_techniques.md#technique-6-prompt-caching)
+
+---
+
+**💡 Tip**: For before/after prompt comparison examples and code templates, refer to [examples.md](examples.md#phase-3-iterative-improvement-examples).