Chain of Responsibility Pattern

The Chain of Responsibility pattern is perfectly suited for LLM applications where requests need to be processed through a series of handlers, each with specific capabilities and decision-making authority.

Why Chain of Responsibility for LLM?

LLM applications often require:

• Sequential processing: Requests flow through multiple AI agents or processing stages

• Conditional handling: Different handlers for different types of queries or complexity levels

• Flexible routing: Dynamic decision-making about which agent handles what

• Fallback mechanisms: Graceful degradation when primary handlers fail

Key LLM Use Cases

1. Multi-Agent Request Routing

The most common application - routing user queries to specialized AI agents:

class AgentChain:
    def __init__(self):
        self.first_handler = None
    
    def add_handler(self, handler):
        if not self.first_handler:
            self.first_handler = handler
        else:
            current = self.first_handler
            while current.next_handler:
                current = current.next_handler
            current.next_handler = handler
    
    def handle_request(self, query):
        if self.first_handler:
            return self.first_handler.handle(query)
        return None

# Specialized agents
math_agent = MathAgent()  # Handles math queries
code_agent = CodeAgent()  # Handles coding questions
general_agent = GeneralAgent()  # Handles everything else

chain = AgentChain()
chain.add_handler(math_agent)
chain.add_handler(code_agent) 
chain.add_handler(general_agent)

Benefits:

• Clear separation of agent responsibilities

• Easy to add/remove specialized agents

• Automatic fallback to general agent

• Request routing based on content analysis

2. Complexity-Based Processing Pipeline

Processing requests based on complexity levels:

class ComplexityChain:
    def handle(self, query):
        complexity = self.analyze_complexity(query)
        
        if complexity == "simple":
            return self.simple_llm.process(query)  # Fast, lightweight model
        elif complexity == "medium":
            return self.medium_llm.process(query)  # Balanced model
        else:
            return self.advanced_llm.process(query)  # Most capable model

# Usage
simple_handler = SimpleQueryHandler()
complex_handler = ComplexQueryHandler()
research_handler = ResearchQueryHandler()

Benefits:

• Cost optimization (use cheaper models for simple queries)

• Performance optimization (faster responses for simple questions)

• Quality assurance (complex queries get the best models)

• Resource management

3. RAG Document Processing Chain

Processing documents through different retrieval and augmentation stages:

class RAGChain:
    def process_query(self, query, documents):
        # Stage 1: Quick keyword search
        if self.keyword_handler.can_handle(query):
            results = self.keyword_handler.search(query, documents)
            if results.confidence > 0.8:
                return results
        
        # Stage 2: Semantic search with embeddings
        if self.semantic_handler.can_handle(query):
            results = self.semantic_handler.search(query, documents)
            if results.confidence > 0.7:
                return results
        
        # Stage 3: Full LLM processing with context
        return self.llm_handler.process_with_full_context(query, documents)

Benefits:

• Performance optimization for different query types

• Progressive enhancement of search quality

• Fallback mechanisms for edge cases

• Adaptive resource usage

4. Content Moderation Pipeline

Processing content through multiple safety and quality checks:

class ModerationChain:
    def moderate_content(self, content):
        # Stage 1: Basic content filter
        if self.basic_filter.is_inappropriate(content):
            return {"status": "blocked", "reason": "basic_filter"}
        
        # Stage 2: AI-powered toxicity detection
        toxicity_score = self.toxicity_detector.analyze(content)
        if toxicity_score > 0.7:
            return {"status": "flagged", "reason": "toxicity"}
        
        # Stage 3: Context-aware moderation
        if self.context_moderator.needs_review(content):
            return {"status": "review", "reason": "context_sensitive"}
        
        return {"status": "approved", "reason": "passed_all_checks"}

Benefits:

• Layered security approach

• Performance optimization (quick filters first)

• Detailed rejection reasoning

• Scalable moderation architecture

5. Error Handling and Recovery Chain

Managing failures and providing alternative responses:

class ErrorRecoveryChain:
    def process_with_recovery(self, query):
        try:
            # Try primary AI service
            return self.primary_ai.process(query)
        except AIServiceError:
            try:
                # Fallback to secondary service
                return self.secondary_ai.process(query)
            except AIServiceError:
                # Final fallback to cached responses
                return self.cache_handler.get_similar_response(query)

Benefits:

• High availability and reliability

• Graceful degradation of service quality

• Multiple backup strategies

• User experience continuity

Implementation Advantages

1. Modularity

• Each handler has a single responsibility

• Easy to test individual components

• Clean separation of concerns

• Independent development of handlers

2. Flexibility

• Runtime chain configuration

• Dynamic handler addition/removal

• Conditional processing paths

• Context-aware routing decisions

3. Scalability

• Easy to add new specialized agents

• Horizontal scaling of handler types

• Load balancing across handlers

• Performance monitoring per handler

4. Maintainability

• Clear request flow visualization

• Easy debugging of processing steps

• Isolated error handling per stage

• Configuration-driven chain setup

Real-World Impact

The Chain of Responsibility pattern in LLM applications provides:

• Cost Efficiency: Route simple queries to cheaper models, complex ones to premium models

• Performance Optimization: Fast responses through appropriate handler selection

• Quality Assurance: Specialized handlers for different domains and complexities

• Reliability: Multiple fallback options and error recovery mechanisms

This pattern is essential for production LLM systems where intelligent request routing, cost optimization, and reliable service delivery are critical requirements.