Towards Retrieval-Augmented Large Language Models: Data Management and System Design

Retrieval-augmented generation (RAG) has become a transformative approach for enhancing large language models (LLMs) by integrating external, reliable, and up-to-date knowledge. This addresses critical limitations such as hallucinations and outdated internal information. This tutorial delves into the evolution and frameworks of RAG, emphasizing the pivotal role of data management technologies in optimizing query processing, storage, indexing, and efficiency. It explores how RAG systems can deliver high-quality, context-aware outputs through efficient retrieval and integration, covering key topics such as retrieval-augmented LLM (RA-LLM) architectures, retrieval techniques, learning methodologies, and applications in NLP and domain-specific tasks. Challenges like customized query and generation, real-time retrieval, and trustworthy RAG are discussed alongside future directions and opportunities for innovation. Designed for students, researchers, and industry practitioners with basic artificial intelligence and data engineering knowledge, this tutorial offers practical insights into designing data management-powered RAG systems. It inspires the exploration of novel solutions in this rapidly evolving field.