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