RAGO introduces a comprehensive framework for optimizing retrieval-augmented generation (RAG) systems, addressing the efficiency challenges in LLM serving with external knowledge.

• RAGSchema: A structured abstraction for characterizing and comparing different RAG variants
• Performance optimization techniques: Tailored to specific RAG workflows and deployment environments
• End-to-end system design: Delivers significant performance improvements across diverse RAG applications
• Practical implementation: Demonstrates real-world efficiency gains for enterprise RAG deployments

This research is particularly valuable for engineering teams building production-ready AI systems, offering a systematic approach to RAG optimization that balances performance with resource utilization.

RAGO: Systematic Performance Optimization for Retrieval-Augmented Generation Serving