Bridging Generative AI and External Knowledge: A Review of Retrieval-Augmented Generation (RAG) and Vector Database Integration
Abstract
Large Language Models (LLMs) have demonstrated strong natural-language generation capability, but knowledge-intensive use remains limited by static parametric knowledge, hallucination, restricted access to proprietary information, and weak evidence traceability. Retrieval-Augmented Generation (RAG) addresses these limitations by retrieving external evidence at inference time, while vector databases provide the storage, indexing, filtering, and similarity-search mechanisms needed to operationalize retrieval at scale. This systematic literature review examines the joint design of RAG pipelines and vector database infrastructure, with emphasis on retrieval architectures, embedding and chunking choices, approximate-nearest-neighbour indexes, reranking, application domains, and end-to-end evaluation. The review was conducted using established systematic-review guidance and reported in accordance with PRISMA 2020. To strengthen analytical depth, the studies were appraised using a structured methodological-quality rubric and synthesized through a multidimensional comparison matrix. The evidence indicates that no single RAG or vector-database configuration dominates across retrieval quality, faithfulness, latency, throughput, storage, cost, and scalability. Building on these findings, this review proposes a unified evaluation framework covering retrieval effectiveness, generation quality, system efficiency, and dynamic-knowledge robustness. Persistent challenges include stale…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.