A trending post on Chinese developer platform Juejin introduces Specification-Driven Development (SDD) as a workflow to control AI code generation. The core idea is to write detailed specifications before asking AI to generate or modify code, reducing the risk of AI hallucination and unintended changes. This approach is particularly relevant as more teams integrate AI coding assistants into their pipelines. The SDD workflow emphasizes human oversight and structured input, contrasting with the common practice of letting AI freely edit code. For overseas developers and tech leads, this signals a growing awareness that AI code generation needs guardrails. The method is not a tool but a process discipline, making it adaptable across different AI coding tools. While the original post is in Chinese, the concept is universal and timely, especially for teams dealing with AI reliability in production. This signal suggests that the community is moving beyond simple prompt engineering toward more systematic workflows.
A new Specification-Driven Development (SDD) workflow helps developers prevent AI from randomly modifying code by defining clear specs before generation. This practical approach addresses AI hallucination in code, gaining traction for production reliability.