Lovable prompts MVP: a step‑by‑step script from idea to production‑ready app

A phased Lovable prompt script for going from idea to a production‑leaning MVP, with Supabase auth/RLS, tests and GitHub export—without burning through credits.

1. What Lovable actually builds well for an MVP

If Lovable is treated as a one-shot generator (“build my SaaS” and hope for the best), the outcome is often a polished demo but a messy codebase. When it is treated as a structured collaborator and driven with a phased prompt script, it can get to a production-leaning MVP: real users, real auth, a real database and a codebase that can be exported to GitHub.

Lovable’s app builder page describes the product as turning natural‑language prompts into working web applications without requiring users to write code.Lovable’s AI app builder description Independent walkthroughs and case studies show the typical stack as a React frontend backed by a Supabase‑based backend for database and auth.LovableForge beginner guide

That stack suits:

CRUD-heavy SaaS products
Internal tools and admin consoles
Simple AI-powered apps that call OpenAI/Anthropic via HTTP

It is web-first: Lovable positions itself as an AI-first builder for web apps, not native mobile binaries, and its own FlutterFlow comparison explains that it does not output iOS/Android store packages.Lovable vs FlutterFlow guide

What Lovable struggles with if prompts are not deliberate:

Complex auth and permissions if proper multi-role behaviour or row level security (RLS) in Supabase are never requested.
Long-lived products without explicit repo hygiene and GitHub handoff prompts.
Credit waste from broad “rebuild everything” prompts instead of narrow, file- or flow-scoped edits.

The rest of this article outlines a scripted prompt workflow that assumes the default Lovable stack (React + Supabase + Lovable Cloud) and the core builder loop (“Plan / Build / Preview / Publish”) described in Lovable’s workspace UI case study.NextLeap Lovable UI study For a deeper product overview of Lovable’s strengths and limitations before you commit to this workflow, read the Lovable review and then return here with that mental model.

2. Pre-work: frame the MVP before you spend credits

Lovable uses an internal credit currency for build/edit prompts, and also allocates credit balances for Lovable Cloud hosting and certain AI features used by deployed apps.Lovable pricing Given the current Free-plan limits on build credits, a disciplined founder can get a first MVP out on Free, but only if they are careful about avoiding wasteful, app-wide rebuild prompts.

An external LLM (ChatGPT, Claude, Gemini, etc.) can be used for planning, where tokens are typically cheaper and mistakes do not consume Lovable credits. If you are still choosing between AI assistants, the Gemini vs ChatGPT comparison walks through practical trade-offs for this kind of planning work.

2.1 Draft a one-page product spec

In an external LLM, use a prompt such as:

Act as a product lead helping me spec a small MVP.

1) Ask me clarifying questions about:
   - target users
   - the main job to be done
   - the 3-5 core features
   - critical constraints (compliance, platforms, integrations)

2) Turn my answers into a 1-page spec:
   - problem
   - target users and roles
   - 3-5 core flows (each as a short story)
   - explicit non-goals for v1
   - assumptions and open questions.

Do NOT write any code. I will use this in Lovable later.

Iterate until the spec is clear and focused. Remove marketing fluff and keep only behaviour.

2.2 Turn it into a “Lovable build script”

Next, ask the external LLM to compress the spec into a numbered build script:

Compress this MVP spec into a Lovable build script.

Constraints:
- <= 300 words
- numbered sections
- each section describes user roles, entities and key flows in neutral terms

The sections I want:
1. Roles and tenants
2. Core entities and relationships
3. Core user flows
4. Non-goals and deferrals
5. Auth and permissions requirements
6. External services (e.g. payments, AI, email)

Save this as Lovable Build Script v1. Sections of it can then be pasted into Lovable prompts instead of re-explaining the app each time.

2.3 Define “done” for a production-leaning MVP

Define three short lists for v1:

Must work: e.g. “Sign up, login, invite users, create/update/delete main entity, view list, basic reporting”.
Must be safe: e.g. “User A cannot see user B’s data; no unauthenticated access; basic rate limits or abuse mitigations”.
Must be verifiable: e.g. “Manual test checklist in Notion; Lovable-generated auth tests; Supabase RLS policies validated manually.”

This checklist becomes a stop condition at the end of each phase.

3. Prompting Lovable for scope and architecture (Plan mode)

Lovable’s core workflow revolves around Plan / Build / Preview / Publish.NextLeap Lovable UI study Plan mode is where credits are best spent on thinking rather than construction.

Lovable AI app builder marketing page section describing how to create an app with AI, with steps for describing the app, watching it build, refining and publishing it. — Lovable’s public app builder page introducing its Plan/Build/Preview/Publish-style workflow, grounding the prompt patterns in this article in the actual stages Lovable exposes to builders.

3.1 First Plan prompt: scope and architecture

In Lovable, create a new app and paste a trimmed version of the build script into Plan mode:

You are designing a production-leaning MVP in Lovable.

Context (Lovable Build Script v1):
[PASTE SECTIONS 1-4 ONLY]

Goals for this Plan step:
1) Propose a high-level architecture for a Lovable web app using React + Supabase.
2) List:
   - all pages/routes
   - main React components per page
   - API endpoints and Supabase tables you expect
3) Call out external services you will use (e.g. Supabase auth/DB, Stripe, email, OpenAI/Anthropic).
4) Highlight anything that looks risky for multi-tenant or multi-role permissions.

Constraints:
- Web app only
- Single region
- Email/password auth only for now
- No paid plans yet (Stripe stubbed but not wired for billing)

Output:
- A numbered list of routes with a 1-2 line description each
- A numbered list of entities and relationships (just names and a sentence)
- A short note on auth and tenancy assumptions
Do NOT start building code yet.

3.2 Verify and iterate the Plan

Review Lovable’s Plan output:

Do routes match the must-have flows?
Are entities named clearly and consistently?
Does it imply one tenant vs multi-tenant? (e.g. “workspace” vs “user-only” design)

If needed, refine with a follow-up Plan prompt:

Revise the Plan with these changes:
- This is a multi-tenant app: users belong to an organisation (tenant), and most data must be tenant-scoped.
- Rename "boards" to "projects" everywhere.
- Remove the "public browse" page.

Update only the lists of routes and entities. Do not start building yet.

Credit hygiene: keep Plan prompts small and focused on lists and assumptions. Avoid “now build everything” while the architecture is still moving.

4. Data model prompts: tables, relations and constraints

Lovable apps are tightly coupled with Supabase for database and authentication, either via Lovable Cloud (a managed Supabase backend) or by connecting to a separate Supabase project.Supabase entry with Lovable mention Prompts are most effective when they talk in terms Supabase understands: tables, columns, types, foreign keys, indexes and RLS policies. If you are not sure whether Supabase is the right backend for you, the standalone Supabase review digs into how it fits AI-built SaaS stacks.

4.1 Ask Lovable for an ERD-style description

In Plan or Build (chat) mode, prompt for a canonical data model:

Using the current Plan (routes and entities), design the Supabase data model.

Output an ERD-style description, not code, with:
- table name
- each column name, type, nullability
- primary key
- foreign keys and relations
- unique constraints and indexes

Constraints:
- All tenant-specific data must reference an organisations table via organisation_id.
- All user-owned data must reference auth.users or a local users profile table via user_id.
- Include created_at and updated_at timestamps.
- Use soft deletes (deleted_at) only where truly needed.

After the ERD, list 5 potential permission or RLS pitfalls that might arise from this model.

4.2 Harden the data model for production basics

Once the ERD looks coherent, ask Lovable to include audit characteristics and non-happy-path considerations:

Adjust the ERD to add minimal production-quality constraints:
- Ensure all foreign keys have ON DELETE behaviour defined.
- Add unique constraints to prevent duplicate organisations, users or core entities.
- Call out which tables should log "who changed what" (e.g. last_modified_by).

Then propose:
- 3 sample SQL queries for key flows
- 1 seed data set per table for local testing

4.3 Verify in Supabase

After Lovable builds the backend (once that step has been triggered), open the Supabase dashboard connected to the project and verify:

Tables and columns match the ERD.
Foreign keys and constraints exist where expected.
Seed data (if generated) appears and is tenant-scoped.

Screenshot to take: Supabase dashboard showing the Lovable-generated tables and any RLS policies, to confirm that the prompts have taken effect in the actual database.

If something is missing, use a narrow Lovable prompt:

In the backend/data layer only, update the Supabase schema to:
- Add a last_modified_by UUID column to <TABLES>
- Enforce unique (organisation_id, name) on the projects table

Show me the migrations or SQL you are applying.
Do not touch frontend code in this change.

5. Screens and flows: from navigation skeleton to usable UI

Many Lovable builds fail because the first prompt mixes “what the app is” with “exact UI look”. Instead, separate navigation, core flows and polish.

5.1 Build a navigation skeleton

Once the Plan and data model are stable, ask Lovable in Build mode:

Create a minimal navigation skeleton for this app.

Use the current Plan routes, and implement:
- top-level layout with header and sidebar
- route definitions for each page
- placeholder components that just render headings and TODO comments

Constraints:
- No detailed styling yet beyond a simple, clean layout
- Use a single layout component and child page components
- Ensure each page has a URL path and a human-readable title

Do not implement forms or business logic in this step.

Preview the app. The full navigation should be visible and clickable, even if pages are empty. If names or hierarchy feel wrong, iterate only on navigation:

Update only the navigation structure:
- Group reporting pages under /reports
- Move organisation settings to /settings/organisation
- Rename "Boards" to "Projects" in nav and headings.

5.2 Implement core flows one by one

Use the build script’s core flows list. For each flow (e.g. “Create project”):

Implement the "Create project" flow end-to-end.

Context:
- A project belongs to an organisation and a creator user.
- Required fields: name, description, status, due_date.

Update only:
- the Create Project page/component
- any API handlers or Supabase calls needed for this flow.

Requirements:
- Form validation with clear error messages
- Loading and success states
- After create, redirect to the project detail page
- Enforce tenant scoping (use organisation_id from the user's session)

Do NOT change navigation or unrelated pages.

Preview and manually test each completed flow before moving on. This “one flow per Build prompt” pattern avoids the wasteful “build the whole app again” approach.

5.3 Introduce a simple design system

Once core flows work, Lovable can be asked to consolidate styling:

Introduce a simple, consistent design system across the app.

Goals:
- One primary button style, one secondary
- Consistent headings and text sizes
- Standard spacing scale

Implementation:
- Create a design tokens file or theme object
- Refactor existing components to use the shared styles

Constraints:
- Avoid complex animations or non-standard UI libraries
- Keep code readable for a future React developer

5.4 Edge states for each flow

After flows work on the happy path, add explicit empty, loading and error states:

For each main list and detail page, add:
- empty state when there is no data (with a call-to-action)
- loading indicator while fetching
- error message when the backend fails

Also:
- ensure errors from Supabase or APIs are logged in a standard way
- avoid swallowing exceptions silently

6. Auth and permissions: production-grade prompts

Lovable integrates directly with Supabase for authentication and can be wired to other third‑party auth providers if the additional configuration is handled manually.Lovable vs FlutterFlow guide For many first MVPs, defaulting to Supabase auth keeps complexity down unless social login or enterprise SSO is explicitly required on day one. For a checklist-style walkthrough of getting Lovable and Supabase talking safely, see the dedicated Lovable + Supabase production setup.

6.1 Decide your auth stack

In many MVPs:

Supabase auth: a common default for email/password, password reset and magic-link style flows.
Other providers: can be considered if SSO or advanced identity management is required and the team is comfortable configuring third-party auth.

In Lovable’s auth configuration UI, select Supabase (or another provider if justified). Screenshot to take: the Lovable auth configuration screen with the chosen provider highlighted.

6.2 Implement basic auth flows

Ask Lovable to wire up core auth flows explicitly:

Implement authentication flows using [Supabase auth OR another configured provider].

Required flows:
- signup with email/password
- login with email/password
- logout
- password reset via email

Requirements:
- Use secure session handling provided by the chosen auth provider.
- Ensure all app routes except /login and /signup require authentication.
- Redirect unauthenticated users to /login.
- After login, redirect to the organisation's dashboard.

Output:
- Updated routes that enforce auth
- Any new components added for auth forms
- A short explanation of how session data is accessed in components

6.3 Prompt for Supabase RLS policies

Row level security is where many “works in demo” apps fall down. Supabase supports RLS policies at the table level.Supabase entry Ask Lovable to generate and apply policies:

Design and apply Supabase Row Level Security (RLS) policies.

Context:
- Users belong to an organisation via organisation_id.
- All sensitive tables (projects, tasks, comments, etc.) must be tenant-scoped.

For each tenant-scoped table:
1) Enable RLS.
2) Add policies so that:
   - authenticated users can SELECT/INSERT/UPDATE/DELETE only rows where organisation_id matches their session organisation_id.
   - optional: admin users in an organisation can manage all rows for that organisation.

Output:
- A table listing each table and its RLS policies in plain English.
- The SQL or migrations used to apply these policies.

Open Supabase and confirm that RLS is enabled for the right tables and that policies exist. Screenshot to take: Supabase RLS policy list for one key table.

6.4 Verify auth and permissions behaviour

Ask Lovable to generate an auth test plan:

Create a manual and automated auth/permissions test plan.

Include:
- A matrix of roles (e.g. anon, user, admin) vs key actions (view, create, update, delete).
- Step-by-step manual test cases for:
   - signup/login/logout
   - password reset
   - creating and viewing tenant data
   - attempting to access another tenant's data
- If possible, automated tests or scripts that assert:
   - 403/401 responses when accessing data from another tenant
   - redirects for unauthenticated access

Output the plan as markdown so I can paste it into our docs.

Run through the manual tests. For any failure, respond in Lovable with a narrow bug prompt, always referencing a specific flow and role.

7. Edge cases and non-happy paths

A production-credible MVP needs explicit error handling and basic instrumentation.

7.1 Design a dedicated edge-case pass

In Plan or Build (chat) mode:

List the top 15 non-happy paths this app is likely to hit.

Group them by category:
- auth and sessions
- permissions and RLS
- form validation
- third-party APIs (payments, AI, email)
- background jobs or long-running tasks

For each, describe:
- what the user experiences today
- what they should experience instead (UX copy, retry, contact support, etc.)

7.2 Implement error handling and logging

Then ask Lovable to address them in code:

Implement better handling for these non-happy paths.

Requirements:
- Add user-visible error messages where failures are currently silent.
- For critical failures, log enough context (user id, organisation id, operation) to debug.
- Use a consistent logging helper instead of console.log scattered everywhere.
- For third-party API failures, implement at least one retry or a clear "try again later" message.

Limit this change to:
- error boundaries
- API route handlers
- shared logging utilities

Do not change core domain logic.

7.3 Minimal instrumentation

Basic health checks can also be requested:

Add a simple /health endpoint and internal status page.

/health should:
- return 200 if the app can reach the database and required services
- include a short JSON payload with status flags (database_ok, auth_ok, etc.)

The internal status page (/internal/status) should be auth-protected and display:
- current user and organisation
- basic counts of key entities (projects, users, etc.)
- a link to documentation and support.

This helps future engineers debug incidents quickly.

8. Testing and verification prompts

Lovable does not promise a formal testing framework out of the box, but it can generate tests and fixtures if asked.

8.1 Generate a test strategy

Start with a high-level plan:

Propose a lean test strategy for this app.

Constraints:
- We want high confidence at low cost.
- Focus on: auth, permissions, and the 3-5 core flows.

Output:
- Which parts should have automated tests vs manual checklists
- Suggested tools or frameworks already available in this Lovable project
- A priority ordering (what to test first)

8.2 Ask for concrete tests and fixtures

Once the strategy is clear, narrow down:

Implement automated tests for the highest-priority flows using the test setup available in this project.

Focus on:
1) Auth: login, logout, access control on a protected route.
2) Permissions: user cannot access another organisation's project.
3) Core CRUD: create, update, delete for the main entity.

Also:
- Add a small set of seed data or fixtures that can be used during local development.
- Document how to run the tests in the README.

Run the tests in Lovable’s environment if available, or after GitHub export (next section) using a preferred CI. If you intend to run AI coding agents against this repo later, read the AI development workflow from prompt to production for a safe pattern that extends this MVP into a GitHub-first stack.

8.3 Manual regression checklist

As a final safety net before launch, ask Lovable for a checkable list:

Generate a concise regression checklist for this MVP.

Structure it as:
- Pre-checks (environment, accounts)
- Core flows
- Auth and permissions
- Edge cases and error messages

Each item should be one line with:
- preconditions
- steps
- expected result

9. GitHub export and handoff-ready prompts

Lovable lets users export apps to a GitHub repo. This is where maintainability is shaped. There is a separate guide dedicated to a Lovable → GitHub production workflow, but the prompts here make that export cleaner.

9.1 Prepare the project for export

Before hitting “Export to GitHub”, ask Lovable to organise and document:

Prepare this project for export to GitHub.

Goals:
- Clear, conventional file and folder structure
- A top-level README that explains how to run the app locally
- A CONTRIBUTING or DEVNOTES file for future developers

Please:
- Group related components, hooks and utilities into sensible folders.
- Remove unused files and dead code paths.
- Add comments at the top of key files explaining their purpose.

In README, include:
- stack overview (React + Supabase + <others>)
- setup steps (environment variables, Supabase, auth provider)
- how to run dev, tests and basic deployment

9.2 Export to GitHub and inspect

Use Lovable’s export flow to create a new GitHub repository linked to the workspace. Screenshot to take: the GitHub repo view of the exported project, showing the folder structure and README.

Once in GitHub:

Skim the README and DEVNOTES for completeness.
Check for any secrets accidentally committed (API keys, connection strings).
Run the app locally and run tests.

If ongoing development will primarily happen in an IDE, this is the point where tools like Cursor or Claude Code become more natural, especially for teams with GitHub-first workflows. For a comparison of those stacks, see Cursor vs Claude Code and pick the editor or terminal-first agent that fits how your team works.

9.3 When to move from Lovable to IDE-driven development

The decision to leave Lovable as the primary builder depends on expected complexity:

If complex, long-lived workflows and strict compliance are expected, export early and move to a GitHub-first stack with Cursor or Claude Code.
If the priority is shipping and making small tweaks without a dedicated engineering team, staying in Lovable and treating GitHub as a backup and collaboration surface can be effective.

10. Estimating and controlling cost with Lovable credits

Lovable uses a credit-based pricing model. Credits are used for build/edit prompts and, via separate balances, for Lovable Cloud hosting and certain AI features used by deployed apps.Lovable pricing The exact per-plan credit allowances and prices should be taken from Lovable’s live pricing page at purchase time; the ranges below are based on third-party breakdowns, not official rate cards.LiveMy pricing breakdown

Third-party pricing guides currently summarise the plans approximately as follows (these are not official Lovable numbers; always verify against lovable.dev/pricing):

Plan	Workspace price (USD/month)	Included build credits/month	Example MVP prompt-pack consumption	Best for
Free	Common third-party breakdowns describe this as $0	A small daily grant with an effective monthly cap (for example, ~5 build credits per day up to around 30 per month)	25–40 build/edit messages for a lean MVP, likely over 2 months	Testing Lovable and slow-building a small MVP
Pro	Third-party analyses commonly describe Pro as roughly $25/month	Often quoted as around 100 build credits with some rollover	40–60 messages for the full script, plus 40–60 buffer for fixes	Non-technical founder shipping one focused MVP
Business	External breakdowns typically quote Business starting around $50+/month	At least as many monthly build credits as Pro, with additional team features	100–150 messages across a team, plus runtime AI usage	Teams iterating on a richer, AI-heavy MVP

10.1 Credits vs phased prompts

The prompt script above breaks the build into roughly these buckets:

Architecture and Plan: 5–10 messages
Data model and Supabase: 5–10 messages
Navigation and core flows: 10–20 messages (one per flow)
Auth and RLS: 5–10 messages
Edge cases and instrumentation: 3–6 messages
Testing and docs: 5–10 messages
GitHub handoff polish: 2–4 messages

That yields a structured range of roughly 35–70 build/edit messages for a disciplined MVP build.

Now compare against indicative plan allowances, using the non-official public credit figures just described (and double-checking against Lovable’s live pricing page before making purchase decisions):

Free: with an effective cap of roughly 30 credits/month in common third-party descriptions, a 35–70 message build would either:
- be split across two months (e.g. 20–25 messages in month one, 15–45 in month two), or
- require trimming the script by cutting back on edge cases and tests, accepting lower confidence.
Pro: at around 100 credits/month as commonly quoted, a full 35–70 message build typically fits comfortably, leaving 30–65 credits for fixes, refactors and runtime AI use in the first month.
Business: with at least as many credits as Pro in most external breakdowns, a small team could run through the entire script and still have capacity for additional features, but should monitor how many credits runtime AI usage consumes.

The main economic benefit of the phased script is that it replaces repeated “rebuild my app” prompts (which might each trigger large internal Plan and Build passes) with targeted edits. That keeps total message count closer to the lower bound (35–50) instead of the upper bound (70+) for a similar functional result.

For deeper pricing scenarios and how runtime AI usage interacts with build credits, see the dedicated Lovable pricing analysis or the broader real cost of building an AI MVP breakdown.

11. When Lovable is the right primary builder—and when it isn’t

This workflow is most effective when:

The goal is a web-first, CRUD-heavy SaaS or internal tool.
Heavy compliance or complex domain logic is not required on day one.
There is a need to manage credits carefully on Free or Pro tiers.

It is less appropriate when:

Native mobile binaries and deep OS integrations are required (consider FlutterFlow or a traditional mobile stack, which Lovable itself contrasts with its web focusLovable vs FlutterFlow guide).
The team already has a strong GitHub- and IDE-first rhythm and wants fine-grained control from day one (Cursor or Claude Code on a handcrafted repo may be cleaner).
The organisation is in a regulated domain with in-house engineers and strict review requirements; Lovable can scaffold, but long-lived compliance usually favours explicit, manual repo hygiene.

For many non-technical founders with CRUD-centric SaaS ideas, the combination of Lovable’s React + Supabase stack,LovableForge beginner guide Supabase auth and integrations like Stripe or AI APIs,Lovable vs FlutterFlow guide plus a disciplined prompt script can be enough to reach a credible production MVP without an initial engineering hire.

12. What changes the decision

The decision to use this Lovable prompt script as the main path to an MVP changes under a few conditions:

Complex, audited workflows and strict compliance: in healthcare or finance with engineers available, Lovable can be used for an initial scaffold, exported to GitHub early, and then followed by a Cursor or Claude Code-led workflow for audits, tests and CI/CD.
Native mobile or offline-first requirements: Lovable is web-only and does not generate mobile binaries,Lovable vs FlutterFlow guide so a mobile-first builder like FlutterFlow or a native stack is more suitable.
GitHub-first teams: if the team lives in GitHub and IDEs and wants architectural control, starting in a conventional repo and using AI coding editors while keeping Lovable as a prototyping option is often cleaner.
Non-technical, credit-sensitive founders: when the main goal is a CRUD SaaS or internal tool, leaning into Lovable with this script and staying on Lovable Cloud until traction is proven can be a pragmatic choice.
AI-heavy runtime costs: if the app will call AI models heavily at runtime, it may be preferable to limit Lovable-hosted AI features and instead integrate external AI APIs directly in the codebase for more transparent per-call economics.Lovable pricing

If none of these conditions apply, the sequenced prompt pack above is a defensible way to go from idea to a production-leaning Lovable MVP, with maintainability and credit efficiency built in from the start.