The article proposes a spec-driven development methodology for business applications that treats requirements, rather than code, as the source of truth. It responds to a common problem in AI-assisted coding: generating code faster can preserve the same long-term drift between requirements, documentation, tests, and implementation. The workflow begins with a business requirements catalog, then uses AI to produce business use case diagrams, entity models, system use cases, detailed Markdown specifications, and application code. Business stakeholders review each stage, including entity models and system use cases, while developers review generated code and architecture. Specifications are stored in Git alongside the code, and diagrams are kept as PlantUML source, enabling visual diffs, audit trails, branching, and collaborative change requests. When requirements change, tools such as Claude Code can update downstream diagrams, models, specifications, code, and traceability links. The approach organizes applications into independent epics, such as event, user, and organization management, to reduce cross-dependencies. An example event-creation specification combines user flows, business rules, and technical validation details so AI has precise implementation guidance. The author reports better business alignment, maintainability, development speed, traceability, and generated test coverage, while arguing that human expertise should handle domain understanding and AI should handle consistent technical implementation.
