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+# Phase 1: Preparation and Analysis Examples
+
+Practical code examples and templates.
+
+**📋 Related Documentation**: [Examples Home](./examples.md) | [Workflow Phase 1](./workflow_phase1.md)
+
+---
+
+## Phase 1: Preparation and Analysis Examples
+
+### Example 1.1: fine-tune.md Structure Example
+
+**File**: `.langgraph-master/fine-tune.md`
+
+```markdown
+# Fine-Tuning Goals
+
+## Optimization Objectives
+
+- **Accuracy**: Improve user intent classification accuracy to 90% or higher
+- **Latency**: Reduce response time to 2.0 seconds or less
+- **Cost**: Reduce cost per request to $0.010 or less
+
+## Evaluation Method
+
+### Test Cases
+
+- **Dataset**: tests/evaluation/test_cases.json (20 cases)
+- **Execution Command**: uv run python -m src.evaluate
+- **Evaluation Script**: tests/evaluation/evaluator.py
+
+### Evaluation Metrics
+
+#### Accuracy (Correctness Rate)
+
+- **Calculation Method**: (Number of correct answers / Total cases) × 100
+- **Target Value**: 90% or higher
+
+#### Latency (Response Time)
+
+- **Calculation Method**: Average time of each execution
+- **Target Value**: 2.0 seconds or less
+
+#### Cost
+
+- **Calculation Method**: Total API cost / Total number of requests
+- **Target Value**: $0.010 or less
+
+## Pass Criteria
+
+All evaluation metrics must achieve their target values.
+```
+
+### Example 1.2: Optimization Target List Example
+
+```markdown
+# Optimization Target Nodes
+
+## Node: analyze_intent
+
+### Basic Information
+
+- **File**: src/nodes/analyzer.py:25-45
+- **Role**: Classify user input intent
+- **LLM Model**: claude-3-5-sonnet-20241022
+- **Current Parameters**: temperature=1.0, max_tokens=default
+
+### Current Prompt
+
+\```python
+SystemMessage(content="You are an intent analyzer. Analyze user input.")
+HumanMessage(content=f"Analyze: {user_input}")
+\```
+
+### Issues
+
+1. **Ambiguous instructions**: Specific criteria for "Analyze" are unclear
+2. **No few-shot examples**: No expected output examples
+3. **Undefined output format**: Free text, not structured
+4. **High temperature**: 1.0 is too high for classification tasks
+
+### Improvement Proposals
+
+1. Specify concrete classification categories
+2. Add 3-5 few-shot examples
+3. Specify JSON output format
+4. Lower temperature to 0.3-0.5
+
+### Estimated Improvement Effect
+
+- **Accuracy**: +10-15% (Current misclassification 20% → 5-10%)
+- **Latency**: ±0 (no change)
+- **Cost**: ±0 (no change)
+
+### Priority
+
+⭐⭐⭐⭐⭐ (Highest priority) - Direct impact on accuracy improvement
+
+---
+
+## Node: generate_response
+
+### Basic Information
+
+- **File**: src/nodes/generator.py:45-68
+- **Role**: Generate final user-facing response
+- **LLM Model**: claude-3-5-sonnet-20241022
+- **Current Parameters**: temperature=0.7, max_tokens=default
+
+### Current Prompt
+
+\```python
+ChatPromptTemplate.from_messages([
+    ("system", "Generate helpful response based on context."),
+    ("human", "{context}\n\nQuestion: {question}")
+])
+\```
+
+### Issues
+
+1. **No redundancy control**: No instructions for conciseness
+2. **max_tokens not set**: Possibility of unnecessarily long output
+3. **Response style undefined**: No specification of tone or style
+
+### Improvement Proposals
+
+1. Add length instructions like "concisely" "in 2-3 sentences"
+2. Limit max_tokens to 500
+3. Clarify response style ("friendly" "professional", etc.)
+
+### Estimated Improvement Effect
+
+- **Accuracy**: ±0 (no change)
+- **Latency**: -0.3-0.5s (due to reduced output tokens)
+- **Cost**: -20-30% (due to reduced token count)
+
+### Priority
+
+⭐⭐⭐ (Medium) - Improvement in latency and cost
+```
+
+### Example 1.3: Code Search Example with Serena MCP
+
+```python
+# Search for LLM client
+from mcp_serena import find_symbol, find_referencing_symbols
+
+# Step 1: Search for ChatAnthropic usage locations
+chat_anthropic_usages = find_symbol(
+    name_path="ChatAnthropic",
+    substring_matching=True,
+    include_body=False
+)
+
+print(f"Found {len(chat_anthropic_usages)} ChatAnthropic usages")
+
+# Step 2: Investigate details of each usage location
+for usage in chat_anthropic_usages:
+    print(f"\nFile: {usage.relative_path}:{usage.line_start}")
+    print(f"Context: {usage.name_path}")
+
+    # Identify prompt construction locations
+    references = find_referencing_symbols(
+        name_path=usage.name,
+        relative_path=usage.relative_path
+    )
+
+    # Display locations that may contain prompts
+    for ref in references:
+        if "message" in ref.name.lower() or "prompt" in ref.name.lower():
+            print(f"  - Potential prompt location: {ref.name_path}")
+```
+
+---