Fill in the form — get a ready-to-use spec. Copy or download the Markdown.
The generator output is meant to be pasted into a ticket, pull request, or design review. It should name the owner, decision, evidence, and follow-up work before implementation starts.
# Feature Spec: Retry failed invoice payment Owner: Billing platform Non-goal: no new payment provider Acceptance Evidence: - duplicate click creates one payment_attempt - provider timeout leaves invoice in retryable state - support UI shows pending provider confirmation
Treat the generated feature spec as a starting artifact. Replace example values, attach evidence, assign reviewers, and remove sections your team will not maintain.
A feature spec is a short, structured document that defines what a feature does, how you know it works (acceptance criteria), and what could go wrong (edge cases) — all before writing any code. It aligns product, engineering, and QA around a shared source of truth.
Writing specs before coding reduces rework, catches misunderstandings early, and makes AI-assisted development more reliable. Instead of vague requirements, your team gets testable, reviewable contracts.
Learn more: What is Spec-First Development? or browse our Feature Spec Template.
Name the product behavior the team is committing to. "Contact deduplication" is stronger than "add a dedupe button" because it leaves room to discuss data rules, automation, permissions, and recovery.
Non-goals protect focus. They tell reviewers what not to request in the first build and give engineers a clear reason to reject scope creep without turning every discussion into a debate.
For every Given/When/Then row, decide how it will be proven: unit test, integration test, contract test, manual QA note, log check, or rollout metric. A criterion without evidence is just intent.
Null input, duplicates, permissions, concurrency, time zones, retry behavior, limits, and rollback are where many "small" features become risky. Listing them early makes tradeoffs visible.
What exactly changes for the user, API, data model, or workflow? What does not change in this iteration?
What examples prove the behavior works? Which failure paths are intentional, and which should block release?
Can the change be rolled out gradually, observed in production, and rolled back without data loss or customer confusion?
Improve the account settings page and make it easier for users to update preferences.
This sounds reasonable, but it does not say which preferences, what "easier" means, which users are included, or how QA should verify success.
Allow workspace admins to update notification defaults for new members. Existing members keep their current settings. Changes are audit logged and visible in the admin activity feed.
This goal names the actor, state boundary, behavior, and verification evidence.
Paste the spec and tell the assistant to implement only the listed goals, avoid non-goals, and produce tests for each acceptance criterion. This keeps the assistant from expanding scope quietly.
Ask for a checklist that maps each code change to a criterion, edge case, or deliverable. If a change cannot be mapped, either remove it or update the spec and re-review.
A typo fix, label correction, or static text update usually needs a small change note, not a full feature spec.
Use a short experiment brief when the goal is learning, not shipping. Turn it into a spec once the behavior is ready to commit.
Internal refactors can use technical design notes instead, unless they affect data, rollout safety, performance, or API contracts.
A good feature spec names what ships now, what waits, which roles are affected, which states change, and how the team knows the slice is complete. It should reduce scope debate during code review.
QA should be able to turn acceptance criteria into test cases without interviewing the author. If the test still depends on a private explanation, the spec is not ready for implementation.
Before reviewing implementation details, ask whether the goal, non-goals, and first shippable slice are explicit. If the scope is still negotiable, the team should resolve that before debating technical design.
Each acceptance criterion should map to a test, screenshot, log line, query, or manual verification step. The generated spec is strongest when reviewers can point to the exact evidence that proves the change is complete.
Long enough to remove ambiguity, short enough for a reviewer to finish in one sitting. For most feature work, one or two pages of precise bullets beats a long narrative that hides the decisions.
Use the Feature Spec Template when you want to write manually in a document. Use this generator when you want a guided form, live preview, draft restore, and quick Markdown export.
The form emphasizes goals, non-goals, acceptance criteria, edge cases, outputs, dependencies, and review questions because those are the gaps that most often turn into rework during implementation.
Generated output is checked by whether a reviewer can identify the first shippable slice, required evidence, excluded work, and AI coding boundaries without needing a follow-up meeting.
Author and editor: Daniel Marsh. Review process: Editorial Policy. Corrections and tool issues: Contact. Last reviewed: April 28, 2026.