In this guide
Lovable turns prompts into full-stack apps, which means the quality of your prompts is the single biggest lever on the quality of your app — and on how many credits you burn getting there. Vague prompts produce vague apps and expensive correction loops; specific, well-structured prompts produce usable builds on the first pass.
This guide collects the prompt patterns that consistently work in Lovable, organised by build stage, with copy-paste examples for each. If you're brand new to Lovable, read our getting started guide first — this one assumes you know the basics of the interface.
The three rules behind every good Lovable prompt
- Be specific about data. 'A task has a title, due date, priority (low/medium/high), and status' beats 'let users manage tasks' every time — the schema Lovable infers from your prompt becomes the foundation everything else sits on.
- One change per message. Scoped prompts produce predictable diffs and cost fewer credits. Batch related tweaks into one message, but never mix unrelated features.
- Say what NOT to do. Lovable fills gaps with assumptions. 'Do not add features I didn't list' and 'keep the design minimal' prevent the most common surprise additions.
Stage 1: The first prompt
Your opening prompt sets the data model, screens, and auth pattern for the whole project. The structure that works: purpose, users, data, screens, rules.
Build a client feedback tracker for freelance designers.
Users sign up with email. Each user only sees their own data.
Core data: a feedback item has a client name, project name, feedback text, status (new / in progress / resolved), and created date.
Screens:
1. Dashboard listing feedback items, filterable by status and client
2. A form to add or edit a feedback item
3. A simple settings page with sign-out
Rules: keep the design clean and minimal. No placeholder content. Do not add features I didn't list.Tip
Naming the exact status values ('new / in progress / resolved') matters — Lovable turns those into a proper enum instead of a free-text field you'll have to fix later.
Stage 2: Adding features without breaking things
After the first build, growth happens one scoped prompt at a time. Two patterns cover most feature additions:
— Adding a field —
Add a 'priority' field to feedback items: low, medium, or high, defaulting to medium. Show it as a colored badge in the dashboard list and add it to the edit form. Don't change anything else.
— Adding a screen —
Add a per-client summary page: clicking a client name on the dashboard opens a page showing all feedback for that client, grouped by status, with counts. Add a back link to the dashboard. No other navigation changes.Stage 3: Auth and data safety
If you didn't request auth in your first prompt, add it explicitly — and always follow up with the security check, because generated policies should be verified, not assumed:
— Adding auth —
Add email sign-up and login. Every existing table gets a user_id column, and each user only sees and edits their own rows. Redirect logged-out visitors to the login page.
— The security check (always run this) —
Audit every database table for per-user data isolation. Confirm Row Level Security is enabled on each, list the policies, and flag any table where one user could read another user's rows. Tell me how to verify this myself with two test accounts.Watch out
The security audit prompt is the single most important one on this page. Our Supabase integration guide explains what the results should look like and how to run the two-account test.
Stage 4: Fixing bugs without loops
The classic Lovable failure mode is the correction loop: a vague bug report, a wrong fix, a re-report, another wrong fix — each round costing credits. Precise bug prompts break the loop:
— Precise bug report —
Bug: when I mark a feedback item as 'resolved' from the dashboard, the badge updates but after refreshing the page it shows 'in progress' again. Expected: the status change persists. Find the root cause and explain it in one sentence before fixing. Do not change anything unrelated.
— When it keeps failing —
Stop. The last fixes didn't work, so the diagnosis is wrong. List the assumptions your previous fix relied on, test the one most likely to be false, and propose a fix based on what you find — not another variation of the same approach.Stage 5: Polish that doesn't cost credits
Remember that Lovable's Visual Edits are free — use them for colours, spacing, and copy instead of chat prompts. Save chat-based polish prompts for the structural passes Visual Edits can't do:
— The three-state pass —
For every screen that displays data, add: a loading skeleton, an empty state with a helpful message and a create button, and an error state with a retry option. Change nothing else.
— The mobile pass —
Audit every page at phone width. Fix horizontal overflow, tap targets smaller than 44px, and any table that should collapse to cards on small screens. List what you fixed.Want the full collection?
These 15 are the Lovable-specific essentials. Our Prompt Vault has the full collection — including security audit, pre-launch checklist, and context-protection prompts that work across Lovable, Cursor, Claude Code, and every other AI coding tool we review.
Key takeaways
- Specific data descriptions in the first prompt prevent the most expensive rework later.
- One scoped change per message keeps diffs predictable and credit costs low.
- Always follow auth changes with the security audit prompt — verify, don't assume.
- Precise bug reports (observed vs expected, root cause first) break the costly correction loop.
- Use free Visual Edits for appearance; save chat prompts for structural changes.
Frequently asked questions
Specificity about data (exact fields and types), one scoped change per message, and explicit boundaries ('do not add features I didn't list'). The first prompt matters most because Lovable builds its data model from it.