From Code to Cognition: Engineering Software Systems with Generative AI and Large Language Models

Abstract

The advent of generative AI and large language models (LLMs) such as GPT and PaLM has redefined the boundaries of software engineering, ushering in a shift from deterministic coding to cognitive, intent-driven system design. These models are no longer just tools—they are collaborators capable of generating, refining, and optimizing code, while also influencing architectural decisions and user interactions. This paper explores the transformative role of LLMs in modern software systems, analyzing how they enable context-aware automation, intelligent assistance, and adaptive behavior across the software development lifecycle. It also discusses the emerging engineering practices, challenges in model alignment and control, and the trade-offs between productivity and system robustness. As software evolves from static logic to dynamic intelligence, a new paradigm is taking shape—where code meets cognition, and engineering becomes as much about orchestrating learning systems as writing syntax.