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
🔗 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.
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