Blueprint for Enterprise LLM Deployment: Real-Time, Regulated, Robust

Deploying Large Language Models (LLMs) in real-time, regulated enterprise environments presents significant challenges, including ensuring knowledge freshness, preventing catastrophic forgetting, mitigating hallucinations, and establishing effective feedback loops. This research proposes a comprehensive, pattern-driven LLMOps architecture specifically designed to overcome these hurdles, offering a blueprint for robust and auditable LLM deployments. The architecture integrates several key components: an Adaptive Ingestion Pattern Orchestrator (AIPO) for real-time data ingestion, a STAR+FAR continual learning mechanism (sparse temporal adapter routing and freshness-aware replay) to maintain up-to-date knowledge, and SAGE, an SLO-aware adaptive retrieval policy for Retrieval-Augmented Generation (RAG) that optimizes for latency. Additionally, an automated feedback-driven convergence stage with Reinforcement Learning from Human Feedback (RLHF) triggers is included. This integrated pipeline aims to reduce latency-cost-accuracy trade-offs while providing the auditability and rollback capabilities essential for high-risk sectors like healthcare and finance.