# Phase 4: Completion and Documentation Examples

Examples of final reports and Git commits.

**📋 Related Documentation**: [Examples Home](./examples.md) | [Workflow Phase 4](./workflow_phase4.md)

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## Phase 4: Completion and Documentation Examples

### Example 4.1: Final Evaluation Report

```markdown
# LangGraph Application Fine-Tuning Completion Report

Project: Customer Support Chatbot
Implementation Period: 2024-11-24 10:00 - 2024-11-24 15:00 (5 hours)
Implementer: Claude Code (fine-tune skill)

## 🎯 Executive Summary

This fine-tuning project optimized the prompts for the LangGraph chatbot application and achieved the following results:

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

A total of 3 iterations were conducted, achieving targets for 2 out of 3 metrics.

## 📊 Implementation Summary

### Number of Iterations and Execution Time

- **Total Iterations**: 3
- **Number of Nodes Optimized**: 2 (analyze_intent, generate_response)
- **Number of Evaluation Runs**: 20 times (Baseline 5 times + 5 times after each iteration × 3)
- **Total Execution Time**: Approximately 5 hours

### Final Results

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

## 📝 Details by Iteration

### Iteration 1: Optimize analyze_intent Node

**Implementation 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 into JSON output format
4. Defined clear classification categories (4 categories)

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

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

---

### Iteration 2: Optimize generate_response Node

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

**Changes**:
1. Added conciseness instructions ("respond 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)

**Learnings**: max_tokens limit significantly contributes to latency and cost reduction

---

### Iteration 3: Additional Improvements to analyze_intent

**Implementation 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. Reclassification logic based on confidence threshold

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

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

## 🔧 Final Changes Summary

### src/nodes/analyzer.py

**Changed Lines**: 25-45

**Main Changes**:
- temperature: 1.0 → 0.3
- Few-shot examples: 0 → 10
- Output: Free text → JSON
- Added fallback based on confidence threshold

---

### src/nodes/generator.py

**Changed Lines**: 45-68

**Main Changes**:
- temperature: 0.7 → 0.5
- max_tokens: unlimited → 500
- Clear conciseness instructions ("2-3 sentences")
- Added response style guidelines

## 📈 Detailed Evaluation Results

### Improvement Status by Test Case

| Case ID | Category  | Before      | After       | Improvement |
| ------- | --------- | ----------- | ----------- | ----------- |
| TC001   | Product   | ❌ Wrong    | ✅ Correct  | ✅          |
| TC002   | Technical | ❌ Wrong    | ✅ Correct  | ✅          |
| TC003   | Billing   | ✅ Correct  | ✅ 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 Rate |
| ----------------- | ------ | ----- | ------ | ----------- |
| 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 Rate |
| ----------------- | ------- | ------- | -------- | ----------- |
| 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 impact on latency

2. **Utilize 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
   - Concise prompts without few-shot examples
   - Estimated effect: -60% cost, +5% accuracy

## 🎓 Conclusion

This project achieved the following through fine-tuning 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 achieved ($0.011 vs $0.010 target) - Can be addressed by migrating to lighter models

📈 **Business Impact**:
- Improved user satisfaction (due to accuracy improvement)
- Reduced operational costs (due to latency and cost reduction)
- Improved scalability (efficient resource usage)

🎯 **Next Steps**:
1. Verify migration to lighter models for cost reduction
2. Continuous monitoring and evaluation
3. Expand to new use cases

---

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

### Example 4.2: Git Commit Message Examples

```bash
# Iteration 1 commit
git commit -m "feat(nodes): optimize analyze_intent prompt for accuracy

- Add temperature control (1.0 -> 0.3) for deterministic classification
- Add 5 few-shot examples for intent categories
- Implement JSON structured output format
- Add error handling for JSON parsing failures

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

Related: fine-tune iteration 1
See: evaluation_results/iteration_1/"

# Iteration 2 commit
git commit -m "feat(nodes): optimize generate_response for latency and cost

- Add conciseness guidelines (2-3 sentences)
- Set max_tokens limit to 500
- Adjust temperature (0.7 -> 0.5) for consistency
- Define response style and tone

Results:
- Accuracy: 86.0% -> 88.0% (+2.0%)
- Latency: 2.4s -> 2.0s (-0.4s, -17%)
- Cost: \$0.014 -> \$0.011 (-\$0.003, -21%)

Related: fine-tune iteration 2
See: evaluation_results/iteration_2/"

# Final commit
git commit -m "feat(nodes): finalize fine-tuning with additional improvements

Complete fine-tuning process with 3 iterations:
- analyze_intent: 10 few-shot examples, confidence threshold
- generate_response: conciseness and style optimization

Final Results:
- Accuracy: 75.0% -> 92.0% (+17.0%, goal 90% ✅)
- Latency: 2.5s -> 1.9s (-0.6s, -24%, goal 2.0s ✅)
- Cost: \$0.015 -> \$0.011 (-\$0.004, -27%, goal \$0.010 ⚠️)

Related: fine-tune completion
See: evaluation_results/final_report.md"

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

- Baseline evaluation (5 iterations)
- Iteration 1-3 results
- Final comprehensive report
- Statistical analysis and recommendations"
```

---

## 📚 Related Documentation

- [SKILL.md](SKILL.md) - Skill overview
- [workflow.md](workflow.md) - Workflow details
- [evaluation.md](evaluation.md) - Evaluation methods
- [prompt_optimization.md](prompt_optimization.md) - Optimization techniques