AI-DRIVEN DEV: BUILDING SMARTER SOFTWARE

The article examines the engineering paradigm of AI-Driven Dev, in which artificial intelligence models are integrated into all stages of the software development life cycle, acting as a governed layer that relies on vetted sources of knowledge, formalized quality loops, and transparent decision rules. The aim of the approach is not accelerated artifact generation; it is higher decision quality, traceability from requirements to implementation, and reduced uncertainty through strict engineering discipline. The relevance of this study is defined by growing pressure to compress the cycle of value delivery, added complexity in technology stacks, and scarcity of narrow expertise. This requires an AI systematic integration in development for the automation of routine work, plus raising communication efficiency and standardizing checks. The novelty of this paper is such that it brings together a knowledge layer, execution layer, quality loops, and data plus security section into one cohesive AI-Driven Dev architecture using Retrieval Augmented Generation on top of enterprise artifacts, deterministic execution scenarios, multi-level validation, inclusive standards for contract testing, observability, and data protection integrated. The main results show that careful integration of AI in the development flow improves engineering validation with static analysis, property-based and mutation testing, increases security plus governance for complex systems, and lowers the chance of building up technical debt. The AI-Driven Dev design assists auto choices to do many checks at different levels before being added inside a setup that can be repeated. It also lets people pick any change and risk control. This paper will help researchers and workers in making software, AI engineering, and big systems design.