Retrieval-Augmented Generation in Enterprise Search: Chunking, Reranking and the Failure Modes Users Notice
DOI:
https://doi.org/10.5281/zenodo.22660464Keywords:
Retrieval-Augmented Generation in Enterprise Search; Applied machine learning; Evidence synthesis; Production deployment; MLOpsAbstract
Retrieval-Augmented Generation in Enterprise Search is an area of active development in applied machine learning, with
evidence that has accumulated unevenly across its component questions. We review the current evidence base for
retrieval-augmented generation in enterprise search, spanning methodological, implementation, governance, and cost dimensions,
focusing on where practice has converged and where it continues to vary, and set out the practical considerations relevant to a
team considering its adoption. We conclude that selective, context-specific use is best supported by the evidence currently
available, rather than uniform adoption or avoidance.
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