Vector Databases for Retrieval-Augmented Generation

Authors

  • Connor Bailey Author

Keywords:

vector databases, retrieval-augmented generation, embeddings, indexing, provenance

Abstract

This review examines vector databases for retrieval-augmented generation. The organizing question is which indexing, update, filtering, provenance, and consistency choices preserve retrieval quality at operational scale. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to evaluating answer quality without isolating retrieval, freshness, and citation failures. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in knowledge-intensive generative systems.

References

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Published

2026-08-08

Issue

Vol. 1 (2026): Archive

Section

Review

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Copyright (c) 2026 Computing Architectures and Data Systems

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