Bridging Symbolic Logic and Neural Intelligence: Hybrid Architectures for Scalable, Explainable AI

Rule-based systems have long served as the foundational architecture for many expert systems and decision engines. However, with the rise of large language models (LLMs), the software engineering landscape is witnessing a paradigm shift. This paper explores the transition from deterministic rule-based methodologies to probabilistic, data-driven models like Transformers and code-oriented LLMs. By analyzing architectural differences, integration strategies, and hybridization potential, we aim to present a roadmap for leveraging the strengths of both paradigms in modern AI-enabled systems. Through comparative insights and system-level evaluations, this study highlights the coexistence and convergence of legacy rule-based engines with cutting-edge AI architectures.