Unleashing the Potential of Large Language Models: A Blueprint for Real-Time, Enterprise-Ready Deployments

Abstract:Large language models deployed in real-time, regulated settings face knowledge staleness, catastrophic forgetting, hallucination, and weak feedback loops. We present a unified, pattern-driven LLMOps architecture integrating real-time data ingestion, continual learning, retrieval-augmented generation (RAG), and human-in-the-loop feedback into a single operational pipeline. Four contributions map to established software design patterns: an adaptive ingestion pattern orchestrator (AIPO) evaluated with FreshStreamBench; STAR+FAR continual learning with sparse temporal adapter routing and freshness-aware replay; SAGE, an SLO-aware adaptive retrieval policy predicting a per-query passage budget to meet tail-latency targets; and an automated feedback-driven convergence stage with RLHF triggers. The result reduces latency-cost-accuracy trade-offs while supporting auditability and rollback for high-risk sectors such as health care and finance.

6 pages, 1 figure. Authors' accepted version of an article published in IEEE Computer. The version of record is available at the DOI below

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)

I.2.7; H.3.3; C.4

arXiv:2608.00419 [cs.LG]

(or arXiv:2608.00419v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.00419 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Computer, vol. 59, no. 4, pp. 195-199, April 2026

https://doi.org/10.1109/MC.2026.3664470 Focus to learn more DOI(s) linking to related resources

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