289 lines
8.9 KiB
Markdown
289 lines
8.9 KiB
Markdown
# Phase 4: Completion and Documentation Examples
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Examples of final reports and Git commits.
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**📋 Related Documentation**: [Examples Home](./examples.md) | [Workflow Phase 4](./workflow_phase4.md)
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---
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## Phase 4: Completion and Documentation Examples
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### Example 4.1: Final Evaluation Report
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```markdown
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# LangGraph Application Fine-Tuning Completion Report
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Project: Customer Support Chatbot
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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 (fine-tune skill)
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## 🎯 Executive Summary
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This fine-tuning project optimized the prompts for the LangGraph chatbot application and achieved the following results:
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- ✅ **Accuracy**: 75.0% → 92.0% (+17.0%, target 90% achieved)
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- ✅ **Latency**: 2.5s → 1.9s (-24.0%, target 2.0s achieved)
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- ⚠️ **Cost**: $0.015 → $0.011 (-26.7%, target $0.010 not achieved)
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A total of 3 iterations were conducted, achieving targets for 2 out of 3 metrics.
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## 📊 Implementation Summary
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### Number of Iterations and Execution Time
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- **Total Iterations**: 3
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- **Number of Nodes Optimized**: 2 (analyze_intent, generate_response)
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- **Number of Evaluation Runs**: 20 times (Baseline 5 times + 5 times after each iteration × 3)
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- **Total Execution Time**: Approximately 5 hours
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### Final Results
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| Metric | Initial | Final | Improvement | Improvement Rate | Target | Achievement Status |
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| -------- | ------- | ------ | ----------- | ---------------- | ------ | ------------------ |
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| Accuracy | 75.0% | 92.0% | +17.0% | +22.7% | 90.0% | ✅ 102.2% |
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| Latency | 2.5s | 1.9s | -0.6s | -24.0% | 2.0s | ✅ 95.0% |
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| Cost/req | $0.015 | $0.011 | -$0.004 | -26.7% | $0.010 | ⚠️ 90.9% |
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## 📝 Details by Iteration
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### Iteration 1: Optimize analyze_intent Node
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**Implementation 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 into JSON output format
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4. Defined clear classification categories (4 categories)
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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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**Learnings**: Few-shot examples and clear output format are most effective for accuracy improvement
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---
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### Iteration 2: Optimize generate_response Node
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**Implementation 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 ("respond 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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**Learnings**: max_tokens limit significantly contributes to latency and cost reduction
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---
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### Iteration 3: Additional Improvements to analyze_intent
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**Implementation 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. Reclassification logic based on 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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**Learnings**: Additional few-shot examples broke through the final accuracy barrier
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## 🔧 Final Changes Summary
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### src/nodes/analyzer.py
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**Changed Lines**: 25-45
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**Main 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 fallback based on confidence threshold
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---
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### src/nodes/generator.py
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**Changed Lines**: 45-68
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**Main Changes**:
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- temperature: 0.7 → 0.5
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- max_tokens: unlimited → 500
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- Clear conciseness instructions ("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 | Improvement |
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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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| ... | ... | ... | ... | ... |
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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 Rate |
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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 Rate |
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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 impact on latency
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2. **Utilize 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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- Concise prompts without few-shot examples
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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 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 achieved ($0.011 vs $0.010 target) - Can be addressed by migrating to lighter models
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📈 **Business Impact**:
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- Improved user satisfaction (due to accuracy improvement)
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- Reduced operational costs (due to latency and cost reduction)
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- Improved scalability (efficient resource usage)
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🎯 **Next Steps**:
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1. Verify migration to lighter models for cost reduction
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2. Continuous monitoring and evaluation
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3. Expand to new use cases
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---
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Created Date/Time: 2024-11-24 15:00:00
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Creator: Claude Code (fine-tune skill)
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```
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### Example 4.2: Git Commit Message Examples
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```bash
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# Iteration 1 commit
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git commit -m "feat(nodes): optimize analyze_intent prompt for accuracy
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- Add temperature control (1.0 -> 0.3) for deterministic classification
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- Add 5 few-shot examples for intent categories
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- Implement JSON structured output format
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- Add error handling for JSON parsing failures
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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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Related: fine-tune iteration 1
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See: evaluation_results/iteration_1/"
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# Iteration 2 commit
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git commit -m "feat(nodes): optimize generate_response for latency and cost
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- Add conciseness guidelines (2-3 sentences)
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- Set max_tokens limit to 500
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- Adjust temperature (0.7 -> 0.5) for consistency
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- Define response style and tone
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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, -17%)
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- Cost: \$0.014 -> \$0.011 (-\$0.003, -21%)
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Related: fine-tune iteration 2
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See: evaluation_results/iteration_2/"
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# Final commit
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git commit -m "feat(nodes): finalize fine-tuning with additional improvements
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Complete fine-tuning process with 3 iterations:
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- analyze_intent: 10 few-shot examples, confidence threshold
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- generate_response: conciseness and style optimization
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Final 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, -24%, goal 2.0s ✅)
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- Cost: \$0.015 -> \$0.011 (-\$0.004, -27%, goal \$0.010 ⚠️)
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Related: fine-tune completion
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See: evaluation_results/final_report.md"
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# Evaluation results commit
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git commit -m "docs: add fine-tuning evaluation results and final report
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- Baseline evaluation (5 iterations)
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- Iteration 1-3 results
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- Final comprehensive report
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- Statistical analysis and recommendations"
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```
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---
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## 📚 Related Documentation
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- [SKILL.md](SKILL.md) - Skill overview
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- [workflow.md](workflow.md) - Workflow details
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- [evaluation.md](evaluation.md) - Evaluation methods
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- [prompt_optimization.md](prompt_optimization.md) - Optimization techniques
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