85 lines
1.8 KiB
Markdown
85 lines
1.8 KiB
Markdown
# Prompt Optimization Principles
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Fundamental principles for designing prompts in LangGraph nodes.
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## 🎯 Prompt Optimization Principles
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### 1. Clarity
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**Bad Example**:
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```python
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SystemMessage(content="Analyze the input.")
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```
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**Good Example**:
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```python
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SystemMessage(content="""You are an intent classifier for customer support.
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Task: Classify user input into one of these categories:
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- product_inquiry: Questions about products or services
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- technical_support: Technical issues or troubleshooting
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- billing: Payment or billing questions
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- general: General questions or greetings
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Output only the category name.""")
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```
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**Improvements**:
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- ✅ Clearly defined role
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- ✅ Specific task description
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- ✅ Enumerated categories
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- ✅ Specified output format
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### 2. Structure
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**Bad Example**:
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```python
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prompt = f"Answer this: {question}"
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```
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**Good Example**:
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```python
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prompt = f"""Context:
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{context}
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Question:
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{question}
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Instructions:
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1. Base your answer on the provided context
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2. Be concise (2-3 sentences maximum)
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3. If the answer is not in the context, say "I don't have enough information"
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Answer:"""
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```
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**Improvements**:
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- ✅ Sectioned (Context, Question, Instructions, Answer)
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- ✅ Sequential instructions
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- ✅ Clear separators
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### 3. Specificity
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**Bad Example**:
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```python
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"Be helpful and friendly."
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```
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**Good Example**:
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```python
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"""Tone and Style:
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- Use a warm, professional tone
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- Address the customer by name if available
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- Acknowledge their concern explicitly
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- Provide actionable next steps
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Example:
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"Hi Sarah, I understand your concern about the billing charge. Let me review your account and get back to you within 24 hours with a detailed explanation."
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"""
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```
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**Improvements**:
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- ✅ Specific guidelines
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- ✅ Concrete examples provided
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- ✅ Measurable criteria
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