Abstract
Large Language Models (LLMs) have rapidly evolved into multi-trillion parameter frontier systems. These systems can perform advanced reasoning, tool integration, and multi modal understanding. Though LLMs have achieved significant advancement, organizations are still facing challenges in how to select, fine-tune, and deploy LLMs for commercial use. In this paper, we present a comprehensive longitudinal study regarding the architecture of LLMs. Our study proposes a practical five-phase framework: Understand, Prepare, Select, Customize, and Productionize. It enables effective use of LLMs within an organizational setting. We will discuss the evolution from Transformer models to frontier and open-source models, and assess their performance across standardized benchmarks. We also evaluate optimization techniques such as: multi-shot prompting, prompt chaining, tool/function calling, retrieval-augmented generation (RAG) and ReAct implemented through Lang Chain. Experimental results of frontier (GPT, Claude, Gemini) and open-source models (LLaMA, Mixtral) reveal that there are trade-offs in accuracy, efficiency, and cost. By comparing them head-to-head in our real use case, we provide both an implementable framework and evidence that targeted optimization reduces their performance gap while keeping overall solution commercially viable.