Spec-Driven Development with LLMs: Writing Requirements That AI Can Implement
How writing precise, structured specifications improves AI code generation outcomes and creates a reviewable contract between intent and implementation.
Vague prompts produce vague code
When a specification is a single sentence, the model fills the gaps with assumptions. A good spec describes inputs, outputs, error cases, side effects, and constraints, leaving less room for the model to guess wrong.
Structure the specification
Use a consistent template: context, requirements, acceptance criteria, and constraints. This structure helps the model produce code that maps to each requirement and helps the reviewer verify that nothing was skipped.
The spec is a review artifact
After implementation, compare the generated code against the spec section by section. Divergences are either bugs in the code or gaps in the spec, and both are worth fixing before the next iteration.
Conclusion
Spec-driven development with AI is not about longer prompts. It is about making intent explicit enough that a machine can implement it and a human can verify the result.
Use AI to expand the amount of thinking your team can verify — never to remove verification from the loop.
