Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 6·5 min read
RAG-Stack: Co-Optimizing RAG Serving Performance and Quality
TL;DR
RAG-Stack is a framework designed to optimize the performance and quality of retrieval-augmented generation (RAG) systems. It significantly outperforms existing methods by covering 52.5% to 153.2% more of the quality-performance space in fewer optimization iterations.
✦ Why It Matters
Implement RAG-Stack to enhance your RAG system's performance and quality today.
Key Takeaways
- Utilize RAG-Stack for efficient RAG system configuration.
- Explore design-space with RAG-PE for optimal settings.
- Leverage RAG-IR for diverse workload management.
- Apply RAG-CM to predict performance on your hardware.
How It Works
RAG-Stack integrates three key components: RAG-PE iteratively explores the design space to identify promising configurations, RAG-IR abstracts the workload for various RAG algorithms to ensure compatibility, and RAG-CM predicts performance outcomes based on the hardware used. This combination allows for a more efficient search for optimal configurations without exhaustive testing.