340 lines
10 KiB
Markdown
340 lines
10 KiB
Markdown
# Phase 4: Completion and Documentation
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Phase to record final results and commit code.
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**Time Required**: 30 minutes - 1 hour
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**📋 Related Documents**: [Overall Workflow](./workflow.md) | [Practical Examples](./examples.md)
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---
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## Phase 4: Completion and Documentation
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### Step 12: Create Final Evaluation Report
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**Report Template**:
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```markdown
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# LangGraph Application Fine-Tuning Completion Report
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Project: [Project Name]
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Implementation Period: 2024-11-24 10:00 - 2024-11-24 15:00 (5 hours)
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Implementer: Claude Code with fine-tune skill
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## Executive Summary
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This fine-tuning project executed prompt optimization for a LangGraph chatbot application and achieved the following results:
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- ✅ **Accuracy**: 75.0% → 92.0% (+17.0%, achieved 90% target)
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- ✅ **Latency**: 2.5s → 1.9s (-24.0%, achieved 2.0s target)
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- ⚠️ **Cost**: $0.015 → $0.011 (-26.7%, target $0.010 not met)
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A total of 3 iterations were executed, achieving 2 out of 3 metric targets.
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## Implementation Summary
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### Iteration Count and Execution Time
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- **Total Iterations**: 3
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- **Optimized Nodes**: 2 (analyze_intent, generate_response)
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- **Evaluation Run Count**: 20 times (baseline 5 times + 5 times × 3 post-iteration)
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- **Total Execution Time**: Approximately 5 hours
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### Final Results
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| Metric | Initial | Final | Improvement | % Change | Target | Achievement |
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|--------|---------|-------|-------------|----------|--------|-------------|
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| Accuracy | 75.0% | 92.0% | +17.0% | +22.7% | 90.0% | ✅ 102.2% achieved |
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| Latency | 2.5s | 1.9s | -0.6s | -24.0% | 2.0s | ✅ 95.0% achieved |
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| Cost/req | $0.015 | $0.011 | -$0.004 | -26.7% | $0.010 | ⚠️ 90.9% achieved |
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## Iteration Details
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### Iteration 1: Optimization of analyze_intent node
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**Date/Time**: 2024-11-24 11:00
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**Target Node**: src/nodes/analyzer.py:25-45
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**Changes**:
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1. temperature: 1.0 → 0.3
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2. Added 5 few-shot examples
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3. Structured JSON output format
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4. Defined clear classification categories (4)
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**Results**:
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- Accuracy: 75.0% → 86.0% (+11.0%)
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- Latency: 2.5s → 2.4s (-0.1s)
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- Cost: $0.015 → $0.014 (-$0.001)
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**Learning**: Few-shot examples and clear output format most effective for accuracy improvement
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---
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### Iteration 2: Optimization of generate_response node
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**Date/Time**: 2024-11-24 13:00
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**Target Node**: src/nodes/generator.py:45-68
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**Changes**:
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1. Added conciseness instructions ("answer in 2-3 sentences")
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2. max_tokens: unlimited → 500
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3. temperature: 0.7 → 0.5
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4. Clarified response style
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**Results**:
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- Accuracy: 86.0% → 88.0% (+2.0%)
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- Latency: 2.4s → 2.0s (-0.4s)
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- Cost: $0.014 → $0.011 (-$0.003)
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**Learning**: max_tokens limit contributed significantly to latency and cost reduction
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---
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### Iteration 3: Additional improvement of analyze_intent
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**Date/Time**: 2024-11-24 14:30
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**Target Node**: src/nodes/analyzer.py:25-45
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**Changes**:
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1. Increased few-shot examples from 5 → 10
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2. Added edge case handling
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3. Re-classification logic with confidence threshold
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**Results**:
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- Accuracy: 88.0% → 92.0% (+4.0%)
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- Latency: 2.0s → 1.9s (-0.1s)
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- Cost: $0.011 → $0.011 (±0)
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**Learning**: Additional few-shot examples broke through final accuracy barrier
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## Final Changes
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### src/nodes/analyzer.py (analyze_intent node)
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#### Before
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```python
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def analyze_intent(state: GraphState) -> GraphState:
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llm = ChatAnthropic(model="claude-3-5-sonnet-20241022", temperature=1.0)
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messages = [
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SystemMessage(content="You are an intent analyzer. Analyze user input."),
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HumanMessage(content=f"Analyze: {state['user_input']}")
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]
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response = llm.invoke(messages)
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state["intent"] = response.content
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return state
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```
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#### After
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```python
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def analyze_intent(state: GraphState) -> GraphState:
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llm = ChatAnthropic(model="claude-3-5-sonnet-20241022", temperature=0.3)
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system_prompt = """You are an intent classifier for a customer support chatbot.
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Classify user input into: product_inquiry, technical_support, billing, or general.
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Output JSON: {"intent": "<category>", "confidence": <0.0-1.0>, "reasoning": "<explanation>"}
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[10 few-shot examples...]
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"""
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messages = [
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SystemMessage(content=system_prompt),
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HumanMessage(content=f"Input: {state['user_input']}\nOutput:")
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]
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response = llm.invoke(messages)
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intent_data = json.loads(response.content)
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# Low confidence → re-classify as general
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if intent_data["confidence"] < 0.7:
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intent_data["intent"] = "general"
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state["intent"] = intent_data["intent"]
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state["confidence"] = intent_data["confidence"]
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return state
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```
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**Key Changes**:
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- temperature: 1.0 → 0.3
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- Few-shot examples: 0 → 10
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- Output: free text → JSON
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- Added confidence threshold fallback
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---
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### src/nodes/generator.py (generate_response node)
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#### Before
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```python
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def generate_response(state: GraphState) -> GraphState:
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llm = ChatAnthropic(model="claude-3-5-sonnet-20241022", temperature=0.7)
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prompt = ChatPromptTemplate.from_messages([
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("system", "Generate helpful response based on context."),
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("human", "{context}\n\nQuestion: {question}")
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])
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chain = prompt | llm
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response = chain.invoke({"context": state["context"], "question": state["user_input"]})
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state["response"] = response.content
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return state
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```
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#### After
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```python
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def generate_response(state: GraphState) -> GraphState:
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llm = ChatAnthropic(
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model="claude-3-5-sonnet-20241022",
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temperature=0.5,
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max_tokens=500 # Output length limit
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)
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system_prompt = """You are a helpful customer support assistant.
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Guidelines:
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- Be concise: Answer in 2-3 sentences
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- Be friendly: Use a warm, professional tone
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- Be accurate: Base your answer on the provided context
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- If uncertain: Acknowledge and offer to escalate
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Format: Direct answer followed by one optional clarifying sentence.
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"""
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prompt = ChatPromptTemplate.from_messages([
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("system", system_prompt),
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("human", "Context: {context}\n\nQuestion: {question}\n\nAnswer:")
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])
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chain = prompt | llm
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response = chain.invoke({"context": state["context"], "question": state["user_input"]})
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state["response"] = response.content
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return state
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```
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**Key Changes**:
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- temperature: 0.7 → 0.5
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- max_tokens: unlimited → 500
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- Clear conciseness instruction ("2-3 sentences")
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- Added response style guidelines
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## Detailed Evaluation Results
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### Improvement Status by Test Case
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| Case ID | Category | Before | After | Improved |
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|---------|----------|--------|-------|----------|
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| TC001 | Product | ❌ Wrong | ✅ Correct | ✅ |
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| TC002 | Technical | ❌ Wrong | ✅ Correct | ✅ |
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| TC003 | Billing | ✅ Correct | ✅ Correct | - |
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| TC004 | General | ✅ Correct | ✅ Correct | - |
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| TC005 | Product | ❌ Wrong | ✅ Correct | ✅ |
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| ... | ... | ... | ... | ... |
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| TC020 | Technical | ✅ Correct | ✅ Correct | - |
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**Improved Cases**: 15/20 (75%)
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**Maintained Cases**: 5/20 (25%)
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**Degraded Cases**: 0/20 (0%)
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### Latency Breakdown
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| Node | Before | After | Change | % Change |
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|------|--------|-------|--------|----------|
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| analyze_intent | 0.5s | 0.4s | -0.1s | -20% |
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| retrieve_context | 0.2s | 0.2s | ±0s | 0% |
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| generate_response | 1.8s | 1.3s | -0.5s | -28% |
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| **Total** | **2.5s** | **1.9s** | **-0.6s** | **-24%** |
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### Cost Breakdown
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| Node | Before | After | Change | % Change |
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|------|--------|-------|--------|----------|
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| analyze_intent | $0.003 | $0.003 | ±$0 | 0% |
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| retrieve_context | $0.001 | $0.001 | ±$0 | 0% |
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| generate_response | $0.011 | $0.007 | -$0.004 | -36% |
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| **Total** | **$0.015** | **$0.011** | **-$0.004** | **-27%** |
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## Future Recommendations
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### Short-term (1-2 weeks)
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1. **Achieve cost target**: $0.011 → $0.010
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- Approach: Consider partial migration to Claude 3.5 Haiku
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- Estimated effect: -$0.002-0.003/req
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2. **Further accuracy improvement**: 92.0% → 95.0%
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- Approach: Analyze error cases and add few-shot examples
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- Estimated effect: +3.0%
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### Mid-term (1-2 months)
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1. **Model optimization**
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- Use Haiku for simple intent classification
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- Use Sonnet only for complex response generation
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- Estimated effect: -30-40% cost, minimal latency impact
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2. **Leverage prompt caching**
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- Cache system prompts and few-shot examples
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- Estimated effect: -50% cost (when cache hits)
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### Long-term (3-6 months)
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1. **Consider fine-tuned models**
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- Model fine-tuning with proprietary data
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- No need for few-shot examples, more concise prompts
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- Estimated effect: -60% cost, +5% accuracy
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## Conclusion
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This project achieved the following through fine-tuning of the LangGraph application:
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✅ **Successes**:
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1. Significant accuracy improvement (+22.7%) - exceeded target by 2.2%
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2. Notable latency improvement (-24.0%) - exceeded target by 5%
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3. Cost reduction (-26.7%) - 9.1% away from target
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⚠️ **Challenges**:
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1. Cost target not met ($0.011 vs $0.010 target) - addressable through migration to lighter models
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📈 **Business Impact**:
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- Improved user satisfaction (through accuracy improvement)
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- Reduced operational costs (through latency and cost reduction)
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- Improved scalability (through efficient resource usage)
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🎯 **Next Steps**:
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1. Validate migration to lighter models for cost reduction
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2. Continuous monitoring and evaluation
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3. Expansion to new use cases
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---
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Created: 2024-11-24 15:00:00
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Creator: Claude Code (fine-tune skill)
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```
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### Step 13: Commit Code and Update Documentation
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**Git Commit Example**:
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```bash
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# Commit changes
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git add src/nodes/analyzer.py src/nodes/generator.py
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git commit -m "feat: optimize LangGraph prompts for accuracy and latency
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Iteration 1-3 of fine-tuning process:
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- analyze_intent: added few-shot examples, JSON output, lower temperature
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- generate_response: added conciseness guidelines, max_tokens limit
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Results:
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- Accuracy: 75.0% → 92.0% (+17.0%, goal 90% ✅)
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- Latency: 2.5s → 1.9s (-0.6s, goal 2.0s ✅)
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- Cost: $0.015 → $0.011 (-$0.004, goal $0.010 ⚠️)
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Full report: evaluation_results/final_report.md"
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# Commit evaluation results
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git add evaluation_results/
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git commit -m "docs: add fine-tuning evaluation results and final report"
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# Add tag
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git tag -a fine-tune-v1.0 -m "Fine-tuning completed: 92% accuracy achieved"
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```
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## Summary
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Following this workflow enables:
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- ✅ Systematic fine-tuning process execution
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- ✅ Data-driven decision making
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- ✅ Continuous improvement and verification
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- ✅ Complete documentation and traceability
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