Ashok Viswanathan, Director of AI & Automation at Dealer Tire, describes how AI-assisted development enabled him to build applications despite a limited technical background. He explains that prompting LLM chat interfaces improved turnaround time for analytics use cases, but sequential prompting became unwieldy as it scaled. A major inflection point came from scanning LinkedIn posts, which led him to the best practice of communicating intent through documentation. The AI assistant drafted the first version of requirements from project goals, and subsequent iterations between documentation refinement and code updates culminated in the Minimum Viable Product (MVP). The leap from MVP to a hosted application was bridged by using the AI assistant for personal projects and expert guidance for enterprise deployments. The AI assistant introduced and compared technologies and explained tradeoffs between platforms and vendors as he reshaped the tech stack. Rapid prototypes, sometimes generated within hours, with frequent feature additions kept him engaged. Despite the AI assistant’s capabilities, constant human-in-the-loop monitoring and strategic guidance were the bridge between requirements and a robust, cohesive product.