gh-hiroshi75-ccplugins-langgraph-master-plugin/skills/fine-tune/workflow_phase3.md

Phase 3: Iterative Improvement

Phase for data-driven, incremental prompt optimization.

Time Required: 1-2 hours per iteration × number of iterations (typically 3-5)

📋 Related Documents: Overall Workflow | Prompt Optimization

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Phase 3: Iterative Improvement

Iteration Cycle

Execute the following in each iteration:

1. Prioritization (Step 7)
2. Implement Improvements (Step 8)
3. Post-Improvement Evaluation (Step 9)
4. Compare Results (Step 10)
5. Continue Decision (Step 11)

Step 7: Prioritization

Decision Criteria:

1. Impact on goal achievement
2. Feasibility of improvement
3. Implementation cost

Priority Matrix:

## Improvement Priority Matrix

| Node | Impact | Feasibility | Impl Cost | Total Score | Priority |
|------|--------|-------------|-----------|-------------|----------|
| analyze_intent | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | 14/15 | 1st |
| generate_response | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | 12/15 | 2nd |
| retrieve_context | ⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | 8/15 | 3rd |

**Iteration 1 Target**: analyze_intent node

Step 8: Implement Improvements

Pre-Improvement Prompt (src/nodes/analyzer.py):

# Before
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

Post-Improvement Prompt:

# After - Iteration 1
def analyze_intent(state: GraphState) -> GraphState:
    llm = ChatAnthropic(
        model="claude-3-5-sonnet-20241022",
        temperature=0.3  # Lower temperature for classification tasks
    )

    # Clear classification categories and few-shot examples
    system_prompt = """You are an intent classifier for a customer support chatbot.

Classify user input into one of these categories:
- "product_inquiry": Questions about products or services
- "technical_support": Technical issues or troubleshooting
- "billing": Payment, invoicing, or billing questions
- "general": General questions or chitchat

Output ONLY a valid JSON object with this structure:
{
  "intent": "<category>",
  "confidence": <0.0-1.0>,
  "reasoning": "<brief explanation>"
}

Examples:

Input: "How much does the premium plan cost?"
Output: {"intent": "product_inquiry", "confidence": 0.95, "reasoning": "Question about product pricing"}

Input: "I can't log into my account"
Output: {"intent": "technical_support", "confidence": 0.9, "reasoning": "Authentication issue"}

Input: "Why was I charged twice?"
Output: {"intent": "billing", "confidence": 0.95, "reasoning": "Question about billing charges"}

Input: "Hello, how are you?"
Output: {"intent": "general", "confidence": 0.85, "reasoning": "General greeting"}

Input: "What's the return policy?"
Output: {"intent": "product_inquiry", "confidence": 0.9, "reasoning": "Question about product policy"}
"""

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

    response = llm.invoke(messages)

    # JSON parsing (with error handling)
    try:
        intent_data = json.loads(response.content)
        state["intent"] = intent_data["intent"]
        state["confidence"] = intent_data["confidence"]
    except json.JSONDecodeError:
        # Fallback
        state["intent"] = "general"
        state["confidence"] = 0.5

    return state

Summary of Changes:

1. ✅ temperature: 1.0 → 0.3 (appropriate for classification tasks)
2. ✅ Clear classification categories (4 intents)
3. ✅ Few-shot examples (added 5)
4. ✅ JSON output format (structured output)
5. ✅ Error handling (fallback for JSON parse failures)

Step 9: Post-Improvement Evaluation

Execution:

# Execute post-improvement evaluation under same conditions
./evaluation_after_iteration1.sh

Step 10: Compare Results

Comparison Report Example:

# Iteration 1 Evaluation Results

Execution Date: 2024-11-24 12:00:00
Changes: Optimization of analyze_intent node

## Results Comparison

| Metric | Baseline | Iteration 1 | Change | % Change | Target | Achievement |
|--------|----------|-------------|--------|----------|--------|-------------|
| **Accuracy** | 75.0% | **86.0%** | **+11.0%** | +14.7% | 90.0% | 95.6% |
| **Latency** | 2.5s | 2.4s | -0.1s | -4.0% | 2.0s | 80.0% |
| **Cost/req** | $0.015 | $0.014 | -$0.001 | -6.7% | $0.010 | 71.4% |

## Detailed Analysis

### Accuracy Improvement
- **Improvement**: +11.0% (75.0% → 86.0%)
- **Remaining gap**: 4.0% (target 90.0%)
- **Improved cases**: Intent classification errors reduced from 12 → 3 cases
- **Still needs improvement**: Context understanding deficiency cases (5 cases)

### Slight Latency Improvement
- **Improvement**: -0.1s (2.5s → 2.4s)
- **Main factor**: Lower temperature in analyze_intent made output more concise
- **Remaining bottleneck**: generate_response (avg 1.8s)

### Slight Cost Reduction
- **Reduction**: -$0.001 (6.7% reduction)
- **Factor**: Reduced output tokens in analyze_intent
- **Main cost**: generate_response still accounts for 73%

## Next Iteration Strategy

### Priority 1: Optimize generate_response
- **Goal**: Latency 1.8s → 1.4s, Cost $0.011 → $0.007
- **Approach**:
  1. Add conciseness instructions
  2. Limit max_tokens to 500
  3. Adjust temperature from 0.7 → 0.5

### Priority 2: Final 4% accuracy improvement
- **Goal**: 86.0% → 90.0% or higher
- **Approach**: Improve context understanding (retrieve_context node)

## Decision
✅ Continue → Proceed to Iteration 2

Step 11: Continue Decision

Decision Criteria:

def should_continue_iteration(results: Dict, goals: Dict) -> bool:
    """Determine if iteration should continue"""
    all_goals_met = True

    for metric, goal in goals.items():
        if metric == "accuracy":
            if results[metric] < goal:
                all_goals_met = False
        elif metric in ["latency", "cost"]:
            if results[metric] > goal:
                all_goals_met = False

    return not all_goals_met

# Example
goals = {"accuracy": 90.0, "latency": 2.0, "cost": 0.010}
results = {"accuracy": 86.0, "latency": 2.4, "cost": 0.014}

if should_continue_iteration(results, goals):
    print("Proceed to next iteration")
else:
    print("Goals achieved - Move to Phase 4")

Iteration Limit:

• Recommended: 3-5 iterations
• Reason: Beyond this, law of diminishing returns likely applies
• Exception: Critical applications may require 10+ iterations