Unleashing the Potential of Large Language Models: A Blueprint for Real-Time, Enterprise-Ready Deployments
Summary
This arXiv paper presents a unified LLMOps architecture for real-time, enterprise-ready LLM deployments, integrating data ingestion, continual learning, RAG, and feedback loops. It introduces components like AIPO, STAR+FAR, and SAGE to address knowledge staleness, hallucination, and latency-cost trade-offs in regulated sectors.
View Cached Full Text
Cached at: 08/04/26, 07:41 AM
# Unleashing the Potential of Large Language Models: A Blueprint for Real-Time, Enterprise-Ready Deployments Source: [https://arxiv.org/abs/2608.00419](https://arxiv.org/abs/2608.00419) [View PDF](https://arxiv.org/pdf/2608.00419) > 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\. ## Submission history From: Muhammad Faizan Raza \[[view email](https://arxiv.org/show-email/71e45c24/2608.00419)\] **\[v1\]**Sat, 1 Aug 2026 03:40:22 UTC \(543 KB\)
Similar Articles
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
DeepSeek LLM is an open-source language model project that develops a large dataset and employs SFT and DPO to achieve performance surpassing LLaMA-2 70B and GPT-3.5 in various benchmarks and open-ended evaluations.
Offline Multimodal Large Language Models for Decision Support in Air Operations
This paper investigates offline multimodal large language models as decision support tools for air operations, detailing a modular retrieval-augmented architecture and presenting a pilot study with the Brazilian Air Force that demonstrates reduced cognitive workload and improved efficiency in doctrinal assessment tasks.
Multi-Stream LLMs: Unblocking Language Models with Parallel Streams of Thoughts, Inputs and Outputs
This paper proposes Multi-Stream LLMs, which transition from sequential message-based instruction tuning to parallel stream processing. This approach allows language models to simultaneously read, think, and generate across multiple concurrent data flows, addressing bottlenecks in autonomous agent applications.
ProactiveLLM: Learning Active Interaction for Streaming Large Language Models
ProactiveLLM introduces a method for streaming LLMs to actively decide when to generate output based on endogenous cues, using mask-based streaming modeling and synchronized privileged self-distillation, reducing latency without external annotations.
Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges
This systematic review examines the applications of large language models in mental health, covering innovations in areas like clinical conversational agents and multimodal learning, while highlighting ethical challenges and advocating for safe deployment frameworks.