Chapter 5

Comprehensive Examples

Apply advanced optimization techniques to real-world scenarios, from Enterprise RAG to Multi-Language Code Generation.

Introduction

This section brings together all the optimization techniques we've explored—COPA, joint optimization, Monte Carlo methods, and Bayesian optimization—through comprehensive, real-world examples.

Example 1: Enterprise RAG System Optimization

We optimize a complex RAG system that needs to answer domain-specific questions accurately while maintaining consistency with company guidelines.

Implementation

class EnterpriseRAGSystem(dspy.Module):
    def forward(self, question, domain=None):
        # 1. Process Query
        enhanced_query = self.query_processor(question, domain)
        
        # 2. Retrieve & Rerank
        docs = self.retriever(enhanced_query).passages
        ranked_docs = self.reranker(enhanced_query, docs)
        
        # 3. Generate Answer
        answer = self.generator(
            question=question,
            context=ranked_docs,
            instruction=self._build_instruction()
        )
        return answer

Multi-Objective Optimization

We use Bayesian Optimization to balance multiple objectives like accuracy, latency, and cost.

optimizer = MultiObjectiveBayesianOptimizer(
    objectives=["accuracy", "latency", "cost"],
    preference_weights={"accuracy": 0.5, "latency": 0.3, "cost": 0.2}
)

Example 2: Multi-Language Code Generation

Optimizing a system that generates code in Python, JavaScript, Java, and C++, using Joint Optimization to transfer knowledge between languages.

class JointCodeOptimizer:
    def optimize(self):
        # Phase 1: Fine-tune individual language models
        for lang in languages:
            self._fine_tune_language_model(lang)
            
        # Phase 2: Joint prompt optimization
        self._optimize_prompts_jointly(shared_knowledge)
        
        return optimized_system
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