Efficient Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Keywords:
retrieval-augmented generation; knowledge-intensive NLP; approximate nearest neighbor search; data governance; sustainable AI; model robustness; socio-technical infrastructureAbstract
Retrieval-augmented generation has emerged as a systems-oriented strategy for addressing knowledge-intensive natural language processing tasks by combining parametric language models with non-parametric retrieval mechanisms. This paper examines the structural trade-offs involved in designing efficient retrieval-augmented generation architectures for deployment across academic, industrial, and public-service settings. Rather than treating retrieval as a simple preprocessing step, the discussion positions retrieval, indexing, generator fusion, and governance as interdependent subsystems whose interaction determines latency, memory use, factual grounding, and long-term maintainability. The paper reviews architectural principles including dense retrieval, late interaction, approximate nearest-neighbor indexing, and generator integration. It then analyzes corpus curation, knowledge provenance, and data governance as first-order engineering concerns rather than secondary documentation tasks. Robustness and fairness are considered across heterogeneous domains, with attention to distribution shift, out-of-domain queries, and the influence of retrieval rankings on generator behavior. Infrastructure dimensions such as accelerator heterogeneity, index sharding, refresh cycles, and carbon-aware operation are discussed in relation to sustainable deployment. Policy implications are developed around auditability, accountability, and multi-institutional governance of retrieval corpora. The paper concludes with future directions for modular verification, self-reflective retrieval decisions, and energy-aware design in knowledge-intensive natural language processing systems.
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