Hybrid AI Reasoning: Integrating Rule-Based Logic with Transformer Inference

As intelligent systems advance, the integration of rule-based logic with transformer-driven neural inference is emerging as a foundational architecture for scalable, explainable AI. This paper explores the shift from static, logic-encoded decision trees to dynamic, context-aware large language models (LLMs), presenting hybrid reasoning systems that combine deterministic control with neural adaptability. We analyze rule-based and transformer-based approaches across key reasoning workflows, supported by architectural diagrams, performance benchmarks, and real-world applications such as policy automation and legal review. Our proposed dual-stream framework illustrates how symbolic validation and generative inference can operate in parallel, enabling trustworthy and adaptable decision-making. The paper also outlines deployment strategies, including tiered inference, observability, and fallback modes, alongside ethical safeguards such as bias audits and transparency checks. These contributions offer a practical blueprint for building hybrid AI systems that are not only performant but also interpretable and governance-ready across diverse enterprise environments.