# Advanced AI/LLM Patterns & Techniques 2025

## 🤖 1. RAG vs Fine-Tuning vs Hybrid (2025 Consensus)

### The 2025 Paradigm Shift
**Old Thinking:** Choose RAG OR fine-tuning
**2025 Reality:** Use HYBRID approach

### When to Use Each

**RAG (Retrieval-Augmented Generation)**
- ✅ Current, frequently-changing data
- ✅ Domain-specific documents
- ✅ Cost-effective (no retraining)
- ✅ Easy to update knowledge
- ✅ For TraceRTM: Requirements, links, history

**Fine-Tuning**
- ✅ Specific writing style/format
- ✅ Rare domain terminology
- ✅ Consistent behavior needed
- ✅ One-time cost, then fast inference
- ✅ For TraceRTM: Requirement analysis patterns

**Hybrid (2025 Best Practice)**
```python
# cli/tracertm/ai/hybrid_approach.py
class HybridAISystem:
    def __init__(self):
        self.rag = RAGSystem()  # For current data
        self.fine_tuned_model = FineTunedModel()  # For patterns
    
    def analyze_requirement(self, requirement: str) -> dict:
        # 1. Use fine-tuned model for initial analysis
        initial_analysis = self.fine_tuned_model.analyze(requirement)
        
        # 2. Use RAG to find similar requirements
        similar = self.rag.search(requirement)
        
        # 3. Combine results
        return {
            "analysis": initial_analysis,
            "similar_requirements": similar,
            "confidence": self._calculate_confidence(initial_analysis, similar)
        }
```

## 🧠 2. Advanced Prompt Engineering (2025)

### Top 8 Techniques (2025 Consensus)

**1. Chain-of-Thought (CoT)**
```python
prompt = """
Analyze this requirement step by step:
1. Identify the main objective
2. List all constraints
3. Identify potential conflicts
4. Suggest improvements

Requirement: {requirement}
"""
```

**2. Few-Shot Prompting**
```python
prompt = """
Examples of good requirements:
- Example 1: [good requirement]
- Example 2: [good requirement]

Now analyze this requirement: {requirement}
"""
```

**3. Meta-Prompting**
```python
prompt = """
First, rewrite this prompt to be clearer:
{original_prompt}

Then use the rewritten prompt to analyze: {requirement}
"""
```

**4. Self-Consistency**
- Generate multiple reasoning paths
- Vote on best answer
- Increases accuracy by 10-20%

**5. Role-Based Prompting**
```python
prompt = """
You are an expert requirements engineer with 20 years of experience.
Analyze this requirement: {requirement}
"""
```

**6. Structured Output**
```python
prompt = """
Analyze this requirement and return JSON:
{
  "clarity": 0-10,
  "completeness": 0-10,
  "testability": 0-10,
  "issues": [],
  "suggestions": []
}

Requirement: {requirement}
"""
```

**7. Constraint-Based Prompting**
```python
prompt = """
Analyze this requirement with these constraints:
- Must be testable
- Must be measurable
- Must be achievable in 2 weeks

Requirement: {requirement}
"""
```

**8. Iterative Refinement**
```python
# First pass: initial analysis
analysis = llm.analyze(requirement)

# Second pass: refine based on feedback
refined = llm.refine(requirement, analysis, feedback)

# Third pass: validate
validated = llm.validate(requirement, refined)
```

## 🔄 3. Agentic Reasoning Patterns

### ReAct (Reasoning + Acting)
```python
class ReActAgent:
    def solve(self, problem: str):
        thought = self.think(problem)
        action = self.decide_action(thought)
        observation = self.execute(action)
        
        if self.is_complete(observation):
            return observation
        else:
            return self.solve_with_context(problem, observation)
```

### Tree-of-Thought (ToT)
```python
class TreeOfThoughtAgent:
    def explore(self, problem: str, depth: int = 3):
        # Generate multiple reasoning paths
        paths = self.generate_paths(problem, depth)
        
        # Evaluate each path
        scores = [self.evaluate(path) for path in paths]
        
        # Return best path
        return paths[scores.index(max(scores))]
```

## 📊 4. GraphRAG (2025 Innovation)

### Beyond Vector Search
```python
# cli/tracertm/ai/graph_rag.py
class GraphRAG:
    def __init__(self):
        self.graph_db = Neo4j()  # Already using!
        self.embeddings = Embeddings()
    
    def search_with_context(self, query: str):
        # 1. Find similar requirements (vector)
        similar = self.embeddings.search(query)
        
        # 2. Find related requirements (graph)
        related = self.graph_db.find_related(similar)
        
        # 3. Build context graph
        context = self.build_context_graph(similar, related)
        
        # 4. Generate answer with full context
        return self.llm.generate(query, context)
```

## 🎯 5. Prompt Caching (2025 Feature)

### Reduce Costs by 90%
```python
# Use with Claude API
response = client.messages.create(
    model="claude-3-5-sonnet",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "You are a requirements engineer"
        },
        {
            "type": "text",
            "text": LARGE_CONTEXT,
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[
        {"role": "user", "content": "Analyze this requirement..."}
    ]
)
```

## 🚀 6. Function Calling & Tool Use

### Structured Tool Integration
```python
tools = [
    {
        "name": "search_requirements",
        "description": "Search for similar requirements",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {"type": "string"},
                "limit": {"type": "integer"}
            }
        }
    },
    {
        "name": "create_link",
        "description": "Create a link between requirements",
        "input_schema": {
            "type": "object",
            "properties": {
                "source_id": {"type": "string"},
                "target_id": {"type": "string"},
                "link_type": {"type": "string"}
            }
        }
    }
]

# Agent uses these tools automatically
agent.run(requirement, tools=tools)
```

## 📈 7. Model Selection (2025)

### For TraceRTM

**Claude 3.5 Sonnet** (Recommended)
- Best reasoning
- Excellent for analysis
- Good cost/performance
- Supports prompt caching

**GPT-4o**
- Multimodal (images, PDFs)
- Good for document analysis
- Slightly faster

**Open Source (Llama 3.1)**
- Privacy-first
- Self-hosted option
- Lower cost at scale

## ✅ IMPLEMENTATION CHECKLIST

- [ ] Implement hybrid RAG + fine-tuning
- [ ] Add chain-of-thought prompting
- [ ] Setup prompt caching
- [ ] Implement function calling
- [ ] Add GraphRAG for context
- [ ] Setup ReAct agent loop
- [ ] Add prompt versioning
- [ ] Monitor token usage

## 📊 EFFORT vs BENEFIT

| Technique | Effort | Benefit | Priority |
|-----------|--------|---------|----------|
| Hybrid RAG | 4 hrs | Very High | 🔥 Critical |
| CoT Prompting | 2 hrs | High | ✅ High |
| Prompt Caching | 1 hr | Very High | 🔥 Critical |
| GraphRAG | 6 hrs | Very High | ✅ High |
| Function Calling | 3 hrs | High | ✅ High |

---

**Status:** Ready for implementation ✅

