Strategy Pattern

The Strategy pattern is ideal for LLM applications where different algorithms or approaches need to be selected dynamically based on context, user preferences, or system requirements.

Why Strategy Pattern for LLM?

LLM applications often require:

• Algorithm selection: Choose between different AI models, prompting strategies, or processing methods

• Runtime flexibility: Switch strategies based on user input, system load, or content type

• A/B testing: Easy experimentation with different approaches

• Context adaptation: Different strategies for different domains, languages, or user types

Key LLM Use Cases

1. Model Selection Strategy

Dynamically choosing the best AI model for different tasks:

class ModelStrategy:
    def execute(self, query, context):
        raise NotImplementedError

class FastModelStrategy(ModelStrategy):
    def execute(self, query, context):
        # Use lightweight, fast model for simple queries
        return gemini_flash.generate(query, max_tokens=100)

class AdvancedModelStrategy(ModelStrategy):
    def execute(self, query, context):
        # Use powerful model for complex reasoning
        return gpt4.generate(query, max_tokens=2000)

class CostOptimizedStrategy(ModelStrategy):
    def execute(self, query, context):
        # Use most cost-effective model
        return claude_haiku.generate(query, max_tokens=500)

class AIAssistant:
    def __init__(self):
        self.strategy = FastModelStrategy()
    
    def set_strategy(self, strategy):
        self.strategy = strategy
    
    def respond(self, query, context):
        return self.strategy.execute(query, context)

# Usage
assistant = AIAssistant()
if context.complexity == "high":
    assistant.set_strategy(AdvancedModelStrategy())
elif context.budget_limited:
    assistant.set_strategy(CostOptimizedStrategy())

Benefits:

• Dynamic model selection based on requirements

• Easy experimentation with different models

• Cost and performance optimization

• Centralized model management

2. Prompting Strategy Selection

Different approaches to prompt engineering:

class PromptStrategy:
    def create_prompt(self, task, context):
        raise NotImplementedError

class FewShotStrategy(PromptStrategy):
    def create_prompt(self, task, context):
        examples = self.get_examples(task.domain)
        return f"""
        Here are some examples:
        {examples}
        
        Now solve: {task.description}
        """

class ChainOfThoughtStrategy(PromptStrategy):
    def create_prompt(self, task, context):
        return f"""
        Let's think step by step about: {task.description}
        
        Step 1: Understand the problem
        Step 2: Break it down
        Step 3: Solve each part
        Step 4: Combine the solution
        """

class ZeroShotStrategy(PromptStrategy):
    def create_prompt(self, task, context):
        return f"""
        Task: {task.description}
        
        Please provide a clear and direct answer.
        """

class PromptEngine:
    def __init__(self):
        self.strategy = ZeroShotStrategy()
    
    def generate_prompt(self, task, context):
        # Select strategy based on task complexity
        if task.complexity == "high" and context.has_examples:
            self.strategy = FewShotStrategy()
        elif task.requires_reasoning:
            self.strategy = ChainOfThoughtStrategy()
        
        return self.strategy.create_prompt(task, context)

Benefits:

• Adaptive prompting based on task requirements

• Easy experimentation with prompt formats

• Consistent prompt structure across strategies

• Performance optimization for different task types

3. RAG Retrieval Strategy

Different approaches to retrieving and ranking relevant information:

class RetrievalStrategy:
    def retrieve(self, query, documents):
        raise NotImplementedError

class KeywordSearchStrategy(RetrievalStrategy):
    def retrieve(self, query, documents):
        # Traditional keyword-based search
        return self.keyword_search(query, documents)

class SemanticSearchStrategy(RetrievalStrategy):
    def retrieve(self, query, documents):
        # Vector-based semantic similarity
        query_embedding = self.embed(query)
        doc_embeddings = [self.embed(doc) for doc in documents]
        return self.find_similar(query_embedding, doc_embeddings)

class HybridSearchStrategy(RetrievalStrategy):
    def retrieve(self, query, documents):
        # Combine keyword and semantic search
        keyword_results = self.keyword_search(query, documents)
        semantic_results = self.semantic_search(query, documents)
        return self.merge_and_rank(keyword_results, semantic_results)

class RAGSystem:
    def __init__(self):
        self.retrieval_strategy = SemanticSearchStrategy()
    
    def search(self, query, documents):
        # Select strategy based on query characteristics
        if self.is_factual_query(query):
            self.retrieval_strategy = KeywordSearchStrategy()
        elif self.is_complex_query(query):
            self.retrieval_strategy = HybridSearchStrategy()
        
        return self.retrieval_strategy.retrieve(query, documents)

Benefits:

• Optimal retrieval for different query types

• Performance tuning for specific domains

• Easy comparison of retrieval methods

• Adaptive search based on content characteristics

4. Response Generation Strategy

Different approaches to generating final responses:

class ResponseStrategy:
    def generate(self, query, context, retrieved_docs):
        raise NotImplementedError

class SummarizeStrategy(ResponseStrategy):
    def generate(self, query, context, retrieved_docs):
        # Summarize information from multiple sources
        combined_info = self.combine_documents(retrieved_docs)
        return self.llm.generate(f"Summarize: {combined_info}")

class SynthesizeStrategy(ResponseStrategy):
    def generate(self, query, context, retrieved_docs):
        # Create new insights from retrieved information
        return self.llm.generate(f"""
        Based on these sources: {retrieved_docs}
        Answer: {query}
        Provide original analysis and insights.
        """)

class DirectAnswerStrategy(ResponseStrategy):
    def generate(self, query, context, retrieved_docs):
        # Provide direct, factual answers
        most_relevant = retrieved_docs[0]
        return self.llm.generate(f"""
        Source: {most_relevant}
        Question: {query}
        Provide a direct, factual answer.
        """)

class ResponseGenerator:
    def respond(self, query, context, docs):
        # Select strategy based on query intent
        if context.intent == "summary":
            strategy = SummarizeStrategy()
        elif context.intent == "analysis":
            strategy = SynthesizeStrategy()
        else:
            strategy = DirectAnswerStrategy()
        
        return strategy.generate(query, context, docs)

Benefits:

• Tailored responses based on user intent

• Consistent response quality across strategies

• Easy testing of different generation approaches

• User preference customization

5. Multi-Language Strategy

Different approaches for handling multiple languages:

class LanguageStrategy:
    def process(self, text, target_language):
        raise NotImplementedError

class TranslateFirstStrategy(LanguageStrategy):
    def process(self, text, target_language):
        # Translate to English, process, translate back
        english_text = self.translate(text, "en")
        result = self.llm.process(english_text)
        return self.translate(result, target_language)

class NativeProcessingStrategy(LanguageStrategy):
    def process(self, text, target_language):
        # Process directly in target language
        prompt = self.create_prompt_in_language(text, target_language)
        return self.multilingual_llm.process(prompt)

class LanguageProcessor:
    def __init__(self):
        self.strategy = NativeProcessingStrategy()
    
    def process_text(self, text, language):
        # Select strategy based on language support
        if language in self.supported_native_languages:
            self.strategy = NativeProcessingStrategy()
        else:
            self.strategy = TranslateFirstStrategy()
        
        return self.strategy.process(text, language)

Benefits:

• Optimal processing for different languages

• Fallback strategies for unsupported languages

• Quality optimization based on language capabilities

• Scalable multi-language support

Implementation Advantages

1. Flexibility

• Runtime strategy switching based on context

• Easy experimentation with different approaches

• User preference customization

• A/B testing capabilities

2. Maintainability

• Clear separation of different algorithms

• Easy to modify or extend individual strategies

• Independent testing of each strategy

• Consistent interface across strategies

3. Performance

• Optimal strategy selection for each scenario

• Resource allocation based on requirements

• Cost optimization through strategy choice

• Performance monitoring per strategy

4. Extensibility

• Easy addition of new strategies

• Plugin-like architecture

• Third-party strategy integration

• Domain-specific strategy customization

Real-World Impact

The Strategy pattern in LLM applications provides:

• Cost Optimization: Choose cost-effective models and approaches based on requirements

• Quality Assurance: Select the best strategy for each specific use case

• User Experience: Personalized responses based on user preferences and context

• System Reliability: Fallback strategies and adaptive behavior

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🔗 Interactive Implementation

📓 Strategy Pattern Notebook - Dynamic provider selection, cost optimization, and intelligent routing strategies in action.

This pattern is crucial for production LLM systems where different scenarios require different approaches, and the ability to switch strategies dynamically is essential for optimal performance and user satisfaction.