saas
Scope and build a SaaS MVP feature end-to-end
Turns a rough SaaS feature idea into a scoped implementation plan and working code, covering data model, backend, UI and edge cases in one pass.
Who should use this
Is this prompt for you?
- Solo founders building a SaaS MVP without a full engineering team
- Developers who want an AI to think through edge cases before writing code
- Teams that want a consistent way to turn a feature idea into a shippable slice
The prompt
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Prompt
Act as a senior full-stack engineer helping me ship a feature for my SaaS product. Product context: - What the product does: [describe your product] - Tech stack: [e.g. Next.js + TypeScript, Postgres via Prisma, Stripe for billing, deployed on Vercel] - Existing auth/user model: [describe briefly, or say "none yet"] Feature to build: [describe the feature in plain language, e.g. "let users invite teammates to their workspace by email"] Before writing any code: 1. Restate the feature in your own words and list any assumptions you're making 2. Propose a data model (tables/columns or schema changes) needed to support it 3. List the API endpoints or server actions required, with their inputs/outputs 4. Flag edge cases I should decide on now (e.g. duplicate invites, expired invites, permission levels, rate limits) Then implement: 5. The database schema/migration 6. The backend logic (API routes or server actions) with input validation and clear error responses 7. A minimal but usable UI for the feature, matching the existing design system if one exists 8. Unit or integration tests covering the happy path and at least 3 edge cases Constraints: - Do not silently make product decisions on ambiguous points — ask me or clearly flag the assumption - Keep the implementation scoped to this feature only; do not refactor unrelated code - Explain any new dependency before adding it - Point out anything here that touches billing, permissions or user data that needs extra security review
Works well with Claude Code, Cursor and OpenAI Codex.
How to use it
Getting the best result
- 1Describe your actual product and stack in the context section — this prompt is meant to be reused across features
- 2Let the AI ask clarifying questions or state assumptions before it writes code; correct any wrong assumptions immediately
- 3Review the proposed data model before implementation starts, since schema changes are the most expensive to undo
- 4Run the generated tests yourself and add a few of your own before shipping
Expected result
What you should get back
- A short written plan: assumptions, data model, endpoints, edge cases
- Working schema changes, backend logic and a basic UI for the feature
- Tests covering the happy path plus edge cases like duplicates, expiry, or permission failures
- An explicit list of security- or billing-sensitive areas to review manually
Tips
Get more out of this prompt
- Reuse this prompt as a template for every new feature so your AI-assisted features follow a consistent shape
- If the feature touches billing or permissions, explicitly ask for a security review pass, don't assume it happened
- Ask for the plan first, review it, then say "proceed with implementation" rather than letting it do everything in one shot
Common mistakes
What to watch out for
- Skipping the planning step and jumping straight to code, which hides bad assumptions until much later
- Not specifying the existing stack, causing the AI to introduce a different framework or ORM than what you use
- Merging the feature without running the generated tests locally first
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Copy promptReady to put this prompt to work?
Pair it with an AI coding tool and a real project, then ship something this week.