The integration of Spec-Driven Development (Spec Kit) into ongoing projects marks a significant advancement in AI-assisted software development. This framework aims to streamline processes by ensuring project standards, functional context, managed decomposition for workload, and quality control through review gates.
However, the real challenge lies in execution, where theoretical ideals often meet practical resistance. While the Spec Kit documentation provides a solid foundation, complete with instructional videos and step-by-step guides, the difficulties arise once teams venture beyond the theoretical sandbox. As with basic object-oriented programming examples, the focus often shifts from syntax to the complexities of real-world software development.
The gap isn't in the documentation itself, but rather in the contextual understanding and expertise required for implementation. Clean examples may suffice for greenfield projects, but most development teams grapple with brownfield codebases that have evolved over months, incorporating various compromises, competing patterns, and unspoken quality standards.
Our experience with Spec Kit unfolded as an honest account of our attempts to navigate these challenges. It is not a polished success story but rather a candid discussion of what worked, what didn’t, and how we made Spec Kit functional within a live production environment where quality compromises were unacceptable.
Our project involved managing an AI productivity portal that has been in production for over a year and a half. This mature system contains 280,000 lines of code and ten active developers, relying on established architecture and quality standards typical of medium-complexity corporate software.
The test case for Spec Kit involved adding a user feedback feature to the portal. The feature allows users to trigger a feedback button that opens a pop-up form where they can select a reaction, add text, and optionally attach a file before sending an email to the support team. It needed to seamlessly integrate with existing usage tracking tools and adhere to our established patterns.
The critical question was whether the Spec Kit workflow could handle integration within an existing project, particularly one that included UI components, external integrations, and stringent quality constraints. Success in this area would validate the approach beyond tutorial scenarios and into real corporate projects facing genuine limitations.
Throughout the implementation, we discovered that Spec Kit requires both human oversight and preliminary efforts, as it does not offer complete automation. Each phase demands focused attention and expertise.
Our initial step involved creating a Constitution—a fundamental document establishing the DNA of the project, outlining standards, agreements, and architectural principles that govern the codebase and the functioning of the AI assistant within it. An initial version might appear comprehensive at first glance, but deeper, project-specific rules are essential for guiding real implementations.
We identified four impactful rule categories for our feedback feature, which can differ based on project needs. These include defining code reuse policies, documenting project-specific architecture, and establishing prohibited patterns to maintain consistency.
For instance, our team quickly recognized that the AI assistant favored creating new code rather than reusing existing components. To address this, we implemented explicit instructions in our Constitution to discourage code duplication and prioritize searching for reusable functions.
Documenting our project's architecture was equally critical. The AI assistant tended to apply common architectural patterns without understanding our specific project needs. We clarified our practices in the Constitution, ensuring the assistant aligned with our unique requirements.
Defining prohibited patterns was another crucial step. For example, we explicitly stated that no try-catch blocks should be used in route handlers, but the assistant still attempted to add them during implementation. By documenting these restrictions, we maintained the integrity of our coding practices.
These insights highlight the necessity of careful documentation and the ongoing role of developers in reviewing the output generated by Spec Kit. The lessons learned from our experience underscore the balance between leveraging AI tools and ensuring adherence to established standards.
As companies explore the potential of Spec-Driven Development, it becomes apparent that success hinges on contextual understanding and the ability to adapt these frameworks to the realities of existing codebases. This approach could redefine how software development teams operate, presenting both new opportunities and challenges for competitors in the market.
Informational material. 18+.
