gh-hiroshi75-protografico-protografico/skills/fine-tune/workflow_phase4.md

Phase 4: Completion and Documentation

Phase to record final results and commit code.

Time Required: 30 minutes - 1 hour

📋 Related Documents: Overall Workflow | Practical Examples

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Phase 4: Completion and Documentation

Step 12: Create Final Evaluation Report

Report Template:

# LangGraph Application Fine-Tuning Completion Report

Project: [Project Name]
Implementation Period: 2024-11-24 10:00 - 2024-11-24 15:00 (5 hours)
Implementer: Claude Code with fine-tune skill

## Executive Summary

This fine-tuning project executed prompt optimization for a LangGraph chatbot application and achieved the following results:

- ✅ **Accuracy**: 75.0% → 92.0% (+17.0%, achieved 90% target)
- ✅ **Latency**: 2.5s → 1.9s (-24.0%, achieved 2.0s target)
- ⚠️ **Cost**: $0.015 → $0.011 (-26.7%, target $0.010 not met)

A total of 3 iterations were executed, achieving 2 out of 3 metric targets.

## Implementation Summary

### Iteration Count and Execution Time
- **Total Iterations**: 3
- **Optimized Nodes**: 2 (analyze_intent, generate_response)
- **Evaluation Run Count**: 20 times (baseline 5 times + 5 times × 3 post-iteration)
- **Total Execution Time**: Approximately 5 hours

### Final Results

| Metric | Initial | Final | Improvement | % Change | Target | Achievement |
|--------|---------|-------|-------------|----------|--------|-------------|
| Accuracy | 75.0% | 92.0% | +17.0% | +22.7% | 90.0% | ✅ 102.2% achieved |
| Latency | 2.5s | 1.9s | -0.6s | -24.0% | 2.0s | ✅ 95.0% achieved |
| Cost/req | $0.015 | $0.011 | -$0.004 | -26.7% | $0.010 | ⚠️ 90.9% achieved |

## Iteration Details

### Iteration 1: Optimization of analyze_intent node

**Date/Time**: 2024-11-24 11:00
**Target Node**: src/nodes/analyzer.py:25-45

**Changes**:
1. temperature: 1.0 → 0.3
2. Added 5 few-shot examples
3. Structured JSON output format
4. Defined clear classification categories (4)

**Results**:
- Accuracy: 75.0% → 86.0% (+11.0%)
- Latency: 2.5s → 2.4s (-0.1s)
- Cost: $0.015 → $0.014 (-$0.001)

**Learning**: Few-shot examples and clear output format most effective for accuracy improvement

---

### Iteration 2: Optimization of generate_response node

**Date/Time**: 2024-11-24 13:00
**Target Node**: src/nodes/generator.py:45-68

**Changes**:
1. Added conciseness instructions ("answer in 2-3 sentences")
2. max_tokens: unlimited → 500
3. temperature: 0.7 → 0.5
4. Clarified response style

**Results**:
- Accuracy: 86.0% → 88.0% (+2.0%)
- Latency: 2.4s → 2.0s (-0.4s)
- Cost: $0.014 → $0.011 (-$0.003)

**Learning**: max_tokens limit contributed significantly to latency and cost reduction

---

### Iteration 3: Additional improvement of analyze_intent

**Date/Time**: 2024-11-24 14:30
**Target Node**: src/nodes/analyzer.py:25-45

**Changes**:
1. Increased few-shot examples from 5 → 10
2. Added edge case handling
3. Re-classification logic with confidence threshold

**Results**:
- Accuracy: 88.0% → 92.0% (+4.0%)
- Latency: 2.0s → 1.9s (-0.1s)
- Cost: $0.011 → $0.011 (±0)

**Learning**: Additional few-shot examples broke through final accuracy barrier

## Final Changes

### src/nodes/analyzer.py (analyze_intent node)

#### Before
```python
def analyze_intent(state: GraphState) -> GraphState:
    llm = ChatAnthropic(model="claude-3-5-sonnet-20241022", temperature=1.0)
    messages = [
        SystemMessage(content="You are an intent analyzer. Analyze user input."),
        HumanMessage(content=f"Analyze: {state['user_input']}")
    ]
    response = llm.invoke(messages)
    state["intent"] = response.content
    return state

After

def analyze_intent(state: GraphState) -> GraphState:
    llm = ChatAnthropic(model="claude-3-5-sonnet-20241022", temperature=0.3)

    system_prompt = """You are an intent classifier for a customer support chatbot.
Classify user input into: product_inquiry, technical_support, billing, or general.
Output JSON: {"intent": "<category>", "confidence": <0.0-1.0>, "reasoning": "<explanation>"}

[10 few-shot examples...]
"""

    messages = [
        SystemMessage(content=system_prompt),
        HumanMessage(content=f"Input: {state['user_input']}\nOutput:")
    ]

    response = llm.invoke(messages)
    intent_data = json.loads(response.content)

    # Low confidence → re-classify as general
    if intent_data["confidence"] < 0.7:
        intent_data["intent"] = "general"

    state["intent"] = intent_data["intent"]
    state["confidence"] = intent_data["confidence"]
    return state

Key Changes:

• temperature: 1.0 → 0.3
• Few-shot examples: 0 → 10
• Output: free text → JSON
• Added confidence threshold fallback

---

src/nodes/generator.py (generate_response node)

Before

def generate_response(state: GraphState) -> GraphState:
    llm = ChatAnthropic(model="claude-3-5-sonnet-20241022", temperature=0.7)
    prompt = ChatPromptTemplate.from_messages([
        ("system", "Generate helpful response based on context."),
        ("human", "{context}\n\nQuestion: {question}")
    ])
    chain = prompt | llm
    response = chain.invoke({"context": state["context"], "question": state["user_input"]})
    state["response"] = response.content
    return state

After

def generate_response(state: GraphState) -> GraphState:
    llm = ChatAnthropic(
        model="claude-3-5-sonnet-20241022",
        temperature=0.5,
        max_tokens=500  # Output length limit
    )

    system_prompt = """You are a helpful customer support assistant.

Guidelines:
- Be concise: Answer in 2-3 sentences
- Be friendly: Use a warm, professional tone
- Be accurate: Base your answer on the provided context
- If uncertain: Acknowledge and offer to escalate

Format: Direct answer followed by one optional clarifying sentence.
"""

    prompt = ChatPromptTemplate.from_messages([
        ("system", system_prompt),
        ("human", "Context: {context}\n\nQuestion: {question}\n\nAnswer:")
    ])

    chain = prompt | llm
    response = chain.invoke({"context": state["context"], "question": state["user_input"]})
    state["response"] = response.content
    return state

Key Changes:

• temperature: 0.7 → 0.5
• max_tokens: unlimited → 500
• Clear conciseness instruction ("2-3 sentences")
• Added response style guidelines

Detailed Evaluation Results

Improvement Status by Test Case

Case ID	Category	Before	After	Improved
TC001	Product	❌ Wrong	✅ Correct	✅
TC002	Technical	❌ Wrong	✅ Correct	✅
TC003	Billing	✅ Correct	✅ Correct	-
TC004	General	✅ Correct	✅ Correct	-
TC005	Product	❌ Wrong	✅ Correct	✅
...	...	...	...	...
TC020	Technical	✅ Correct	✅ Correct	-

Improved Cases: 15/20 (75%) Maintained Cases: 5/20 (25%) Degraded Cases: 0/20 (0%)

Latency Breakdown

Node	Before	After	Change	% Change
analyze_intent	0.5s	0.4s	-0.1s	-20%
retrieve_context	0.2s	0.2s	±0s	0%
generate_response	1.8s	1.3s	-0.5s	-28%
Total	2.5s	1.9s	-0.6s	-24%

Cost Breakdown

Node	Before	After	Change	% Change
analyze_intent	$0.003	$0.003	±$0	0%
retrieve_context	$0.001	$0.001	±$0	0%
generate_response	$0.011	$0.007	-$0.004	-36%
Total	$0.015	$0.011	-$0.004	-27%

Future Recommendations

Short-term (1-2 weeks)

1. Achieve cost target: $0.011 → $0.010

   • Approach: Consider partial migration to Claude 3.5 Haiku
   • Estimated effect: -$0.002-0.003/req

2. Further accuracy improvement: 92.0% → 95.0%

   • Approach: Analyze error cases and add few-shot examples
   • Estimated effect: +3.0%

Mid-term (1-2 months)

1. Model optimization

   • Use Haiku for simple intent classification
   • Use Sonnet only for complex response generation
   • Estimated effect: -30-40% cost, minimal latency impact

2. Leverage prompt caching

   • Cache system prompts and few-shot examples
   • Estimated effect: -50% cost (when cache hits)

Long-term (3-6 months)

1. Consider fine-tuned models
   • Model fine-tuning with proprietary data
   • No need for few-shot examples, more concise prompts
   • Estimated effect: -60% cost, +5% accuracy

Conclusion

This project achieved the following through fine-tuning of the LangGraph application:

✅ Successes:

1. Significant accuracy improvement (+22.7%) - exceeded target by 2.2%
2. Notable latency improvement (-24.0%) - exceeded target by 5%
3. Cost reduction (-26.7%) - 9.1% away from target

⚠️ Challenges:

1. Cost target not met ($0.011 vs $0.010 target) - addressable through migration to lighter models

📈 Business Impact:

• Improved user satisfaction (through accuracy improvement)
• Reduced operational costs (through latency and cost reduction)
• Improved scalability (through efficient resource usage)

🎯 Next Steps:

1. Validate migration to lighter models for cost reduction
2. Continuous monitoring and evaluation
3. Expansion to new use cases

---

Created: 2024-11-24 15:00:00 Creator: Claude Code (fine-tune skill)

### Step 13: Commit Code and Update Documentation

**Git Commit Example**:
```bash
# Commit changes
git add src/nodes/analyzer.py src/nodes/generator.py
git commit -m "feat: optimize LangGraph prompts for accuracy and latency

Iteration 1-3 of fine-tuning process:
- analyze_intent: added few-shot examples, JSON output, lower temperature
- generate_response: added conciseness guidelines, max_tokens limit

Results:
- Accuracy: 75.0% → 92.0% (+17.0%, goal 90% ✅)
- Latency: 2.5s → 1.9s (-0.6s, goal 2.0s ✅)
- Cost: $0.015 → $0.011 (-$0.004, goal $0.010 ⚠️)

Full report: evaluation_results/final_report.md"

# Commit evaluation results
git add evaluation_results/
git commit -m "docs: add fine-tuning evaluation results and final report"

# Add tag
git tag -a fine-tune-v1.0 -m "Fine-tuning completed: 92% accuracy achieved"

Summary

Following this workflow enables:

• ✅ Systematic fine-tuning process execution
• ✅ Data-driven decision making
• ✅ Continuous improvement and verification
• ✅ Complete documentation and traceability