A vague task leaves too much room for interpretation – and that becomes especially noticeable when AI is writing the code.
Spec-Driven Development takes a more structured approach: define the requirements, constraints, and expected behavior first, and use them as a reference during implementation.
In the article, we look at this through a practical example of integration monitoring. What exactly should the monitoring system show? What should happen when there’s no data? What counts as an error? Where does reporting a problem end and fixing it begin? Defining these details upfront gives the AI agent less room to make assumptions.
That doesn’t mean writing huge specs. The level of detail should match the complexity and risks of the task.
We also cover two more practical cases: PDF generation and a logistics status system.
How do you usually work with AI agents: define the requirements in detail upfront, or refine the result as you go?
Spec-Driven Development takes a more structured approach: define the requirements, constraints, and expected behavior first, and use them as a reference during implementation.
In the article, we look at this through a practical example of integration monitoring. What exactly should the monitoring system show? What should happen when there’s no data? What counts as an error? Where does reporting a problem end and fixing it begin? Defining these details upfront gives the AI agent less room to make assumptions.
That doesn’t mean writing huge specs. The level of detail should match the complexity and risks of the task.
We also cover two more practical cases: PDF generation and a logistics status system.
How do you usually work with AI agents: define the requirements in detail upfront, or refine the result as you go?