8.9 KiB
8.9 KiB
Phase 4: Completion and Documentation Examples
Examples of final reports and Git commits.
📋 Related Documentation: Examples Home | Workflow Phase 4
Phase 4: Completion and Documentation Examples
Example 4.1: Final Evaluation Report
# 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
# 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 overview
- workflow.md - Workflow details
- evaluation.md - Evaluation methods
- prompt_optimization.md - Optimization techniques