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

Researchers propose a unified LLMOps architecture for real-time, enterprise-ready deployments of large language models, addressing knowledge staleness, catastrophic forgetting, hallucination, and weak feedback loops. The system integrates real-time data ingestion, continual learning, retrieval-augmented generation, and human-in-the-loop feedback, with components including an adaptive ingestion pattern orchestrator (AIPO), STAR+FAR continual learning, SAGE retrieval policy, and an automated feedback-driven convergence stage. The approach aims to reduce latency-cost-accuracy trade-offs while supporting auditability and rollback for high-risk sectors such as health care and finance.

arXiv:2608.00419v1 Announce Type: new 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.