Operationalizing Large Language Models for Knowledge Synthesis and Performance

Operationalizing Large Language Models (LLMs) within enterprise decision architectures presents a paradigm shift for organizational knowledge synthesis and strategic intelligence. Prior to 2020, enterprise data architecture relied primarily on structured business intelligence systems and deterministic rule engines, which failed to effectively process, synthesize, and contextualize large volumes of unstructured enterprise data. This study investigates the integration of pre-trained Transformer architectures—specifically autoregressive systems such as GPT-3 and masked models such as BERT—into enterprise decision pipelines. We propose a Retrieval-Augmented Generation (RAG) framework incorporating Dense Passage Retrieval (DPR) and sparse BM25 indexing, coupled with a non-linear decision validation bounds model adapted from foundational non-linear estimation theory (Haque & Rasel-Ul-Alam, 2018). Using a standardized synthetic enterprise benchmark dataset (N = 10,000 decision tasks; M = 500,000 document vectors), we systematically evaluate the performance trade-offs among model parameter scale (1.5B to 175B parameters), sequence context window length (512 to 4096 tokens), inference latency, and hallucination density. The empirical findings demonstrate that hybrid retrieval architectures yield superior semantic alignment, achieving a 28.4% increase in Recall@10 and a 21.3% reduction in hallucination density over parametric-only generative models. Furthermore, incorporating non-linear confidence bounds provides robust quantitative guardrails against output drift in high-dimensional decision spaces. This research offers an enterprise decision architecture that bridges theoretical information processing limits with scalable, generative AI capabilities, establishing actionable frameworks for executive leadership and data architects.