Spec-driven development (SDD) is a software workflow in which an explicit, editable specification guides an AI coding agent from requirements through design, implementation, and validation. Instead of relying on a single broad prompt, a team makes expected behavior and acceptance criteria visible, turns them into tasks, and checks the resulting code against them. The specification gives the agent more durable context, but it does not guarantee correct code.
What spec-driven development means
In SDD, the specification is a working guide to what the software should do and how the work will be checked. It is not simply a long prompt, nor is it a document that must be completed once and treated as infallible. People and agents can refine it as they uncover ambiguity or learn that a requirement or design needs to change.
GitHub Spec Kit describes a workflow that carries intent through specification, planning, tasks, implementation, and convergence. Kiro’s feature-spec workflow uses requirement, design, and task artifacts. Both provide structure around the agent’s work; neither, by itself, establishes that the output is correct or that SDD outperforms another development approach.
How an AI-assisted SDD workflow works
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Describe the outcome and constraints
Explain the user-visible behavior, scope, important edge cases, and constraints. Keep consequential unknowns explicit rather than letting the agent silently pick an interpretation. GitHub’s introduction frames the goal as turning vague prompts into clear intent: GitHub’s Spec Kit announcement.
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Write and refine requirements
Express expected behavior in observable terms and define acceptance criteria that can be checked. Kiro documents EARS-style requirements, which use conditional phrasing to make system behavior explicit. Ask the agent to identify ambiguity, contradictions, and missing cases, then review and edit the resulting requirements rather than accepting them automatically. See Kiro’s feature-spec documentation.
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Choose whether to start with requirements or design
Use a requirements-first path when the desired behavior is known but the technical approach remains open. Use a design-first path when an existing architecture, pseudocode, or strict nonfunctional constraint already limits what can be built. The right starting point depends on what the team knows; it is not a universal rule that one path must always come first. Kiro documents both approaches in its feature-spec guidance.
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Break the work into tasks
Turn requirements and design decisions into discrete tasks that can be assigned, tracked, and checked. Keep dependencies and relevant acceptance criteria visible so reviewers can see which part of the specification each task serves. GitHub Spec Kit and Kiro both include task-oriented artifacts in their documented workflows: GitHub Spec Kit documentation and Kiro feature specs.
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Implement with the artifacts in context
Give the agent the relevant requirements, design, and tasks while it works. Review its changes, and revise the artifacts when implementation exposes a genuine requirement or design issue. Treat the specification as a maintained working contract, not as proof that every generated change matches the intended product.
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Validate, then converge
Run appropriate tests, inspect the implementation against each acceptance criterion, and revise either the code or the specification where needed. Kiro describes optional property-based tests linked to requirements and tasks. Such tests can reveal failures, but a passing test suite is evidence rather than proof: the tested property may be too weak or may not capture the requirement. Kiro explains this limitation in its correctness documentation.
How much process should you use?
Choose the amount of review according to uncertainty and the cost of a mistake. Kiro describes standard specs as suited to work where iteration and review matter, and Quick Spec as an accelerated option that skips approval gates between generated requirements, design, and tasks while keeping artifacts editable. These are the vendor’s descriptions of its workflows, not independent evidence that either approach produces better outcomes.
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- Use explicit gates when requirements are unfamiliar, important behaviors interact, or reliability and compliance consequences are significant. Review requirements before design and design before implementation when those checkpoints could catch costly misunderstandings.
- Use a lighter workflow for well-understood changes when the team is prepared to review the generated artifacts afterward.
- Add orchestration selectively for larger tasks that benefit from sequential steps, independent review, and validation. Kiro notes that multi-step workflows use more tokens than a single session, so the extra coordination should earn its cost.
For Kiro’s workflow options and guidance, see spec best practices and workflow documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to evaluate when choosing a workflow
Compare the options against the work in front of you rather than adopting a process for its label. The useful questions are whether behavior or architecture is already known, how uncertain the requirements are, how costly errors would be, whether phases need explicit approval, whether artifacts stay editable and traceable, how acceptance criteria will be validated, and how much coordination the team and agent can support.
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Those factors distinguish requirements-first from design-first work, and a quick generated plan from a review-gated one. A workflow should make important decisions easier to inspect without creating unnecessary process for a small, familiar change.
What SDD does—and does not—establish
SDD makes stated intent and acceptance criteria guide implementation. It does not mean “write a long prompt and trust the output,” and it does not make an agent’s assumptions, design, or code correct merely because they appear in structured artifacts. Tests are only as meaningful as the behaviors they actually represent.
Official tool documentation explains features and intended workflows; it does not establish that SDD universally raises quality or productivity. No topic-specific published statistic in the cited sources supports a productivity percentage or measured quality gain. Teams that want to assess results can compare their own baseline using measures such as missed acceptance criteria, escaped defects, rework, review time, and end-to-end delivery time. Those are possible evaluation measures, not published findings.
Sources: GitHub Spec Kit documentation; GitHub’s announcement; Kiro feature specs; Kiro best practices; Kiro correctness; and Kiro workflows.
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