Why Spec-Driven Development May Be Counterproductive in AI Coding

Spec-Driven Development (SDD) is a methodology that suggests writing a detailed specification or system design document before using AI to generate code, hoping the AI will produce high‑quality code in one shot.

Critics, such as the community figure Baoyu, argue that this approach resembles a waterfall model: it imposes heavy cognitive load by forcing developers to define every interface, exception handling, and state transition up front, which defeats the purpose of AI assistance.

Two main drawbacks are highlighted:

Cognitive overload : Developers spend excessive effort drafting exhaustive specs, exhausting themselves before any code is written.

Loss of AI flexibility : AI’s strengths are speed and plasticity; spending two hours on a spec can cause developers to miss the chance to quickly validate a minimal viable product in ten minutes.

As an alternative, Baoyu proposes Small Iterations , a workflow that does not require lengthy specifications. The steps are:

Write a few simple prompts describing what you want to achieve.

Generate a runnable version of the code—prioritise "can run" over perfection.

Iterate based on the execution results, fixing errors and improving the experience where needed.

This approach leverages AI’s ability to provide rapid trial‑and‑error cycles, ensuring each iteration yields a functional program.

The article also references Claude Code’s Plan Mode , which creates a concise plan document based on the desired functionality and allows repeated confirmation with the AI, avoiding the need for a massive spec.

In summary, when AI coding forces developers to write longer prompts and specifications than the code itself, it risks reverting to an outdated waterfall process rather than exploiting the agility of modern AI tools.