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arXiv · 2608.00419

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

Muhammad Faizan Raza·Shuo·Yang·Satish Mahadevan Srinivasan·Joanna F. DeFranco
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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.

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Muhammad Faizan Raza, Shuo, Yang, Satish Mahadevan Srinivasan, Joanna F. DeFranco. 2026-08-01. Unleashing the Potential of Large Language Models: A Blueprint for Real-Time, Enterprise-Ready Deployments. https://doi.org/10.1109/mc.2026.3664470

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