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

• URL: http://arxiv.org/abs/2608.00419v1
• Date: Sat, 01 Aug 2026 03:40:22 GMT
• Title: Unleashing the Potential of Large Language Models: A Blueprint for Real-Time, Enterprise-Ready Deployments
• Abstract summary: We present a unified, pattern-driven LLMOps architecture integrating real-time data ingestion, continual learning, retrieval-augmented generation, and human-in-the-loop feedback.<n>The result reduces latency-cost-accuracy trade-offs while supporting auditability and rollback for high-risk sectors such as health care and finance.
• Score: 11.71713024399078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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