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 embeddings, index-update cost, knowledge freshness, security, privacy, explainability, and inconsistent benchmarking. The review therefore positions RAG–vector database integration as a joint retrieval-and-systems optimization problem rather than a database-selection problem alone.